Patent Publication Number: US-9836533-B1

Title: Apparatus, method and article to effect user interest-based matching in a network environment

Description:
BACKGROUND 
     Technical Field 
     The present disclosure generally relates to computing systems and methods, and in particular to systems and methods that facilitate interest-based matching of users within a user network. 
     Description of the Related Art 
     Pairing or match-making finds use and purpose in many areas, from the smallest of interpersonal relationships to the largest of commercial partnerships. Pairing or matching may be used for finding compatible individuals suitable for a particular position or job, or even finding matching goods (e.g., consumer goods, movies, music) or services with individuals based on the preferences of the individual. With the extraordinary reach of the Internet into virtually every country on the planet and the computational power of modern-day processors, the extension of personal and commercial pairing or match-making into the digital world could be viewed as inevitable. 
     Typically, a user begins participating in such match-matching or pairing Websites by providing information used by the Website in selecting or otherwise identifying candidate matches or pairings for the user. Such information is typically collected during a registration process performed by the user and may include a variety of data inputs provided by the user, such as in the form of a “personality profile” or “interests profile.” At times, such information may be provided by the user as freeform text, i.e., text entered by the user that is not format constrained. For example, the Website operator or pairing service provider may provide the user with a Webpage containing a text box and ask “Please tell us about the activities you enjoy.” In response, a first user may enter “snow skiing, snowboarding, and ice climbing” while a second user may enter “water skiing, swimming, snorkeling, and scuba diving.” 
     When confronted with such freeform text entries, traditional pairing or matching algorithms may search for words common to both users as indicative of a candidate pairing. In the preceding example, the term “skiing” appears in the freeform text entry provided by both the first user and the second user. However, the freeform entry of the first user indicates a preference for cold weather activities while the freeform entry of the second user indicates a preference for warm weather activities. While a pairing between the first and second users may be possible, the fact that the users have indicated different seasonal activities increases the likelihood that the pairing is somewhat less than ideal. 
     What is needed therefore are automated entity pairing systems and methods that are capable of matching or otherwise pairing entities having near, but non-matching, attributes or dissimilar attributes that may have been found compatible based on historically successful matches or pairings. 
     BRIEF SUMMARY 
     Websites, such as relationship building, matching, or pairing Websites collect a wealth of data on Website users, typically as part of the relationship building or matching process. At times, a first portion of such collected data may be self-reported by the respective user while a second portion of such data may be autonomously collected by a server or other system housing the Website, for example such data may be extracted from the user&#39;s processor-based device communicating with the Website. In some instances, users may enter freeform text, for example using a keyboard or keypad to enter text into a text field or similar logical structure. Such freeform text is often used to provide the user with the ability to enter interests, goals, desires, and similar profile aspects that are difficult or impossible to capture using more traditional radio-button or “check box” type inputs. Quite often, the user input provided as freeform text supplies key insights into a user&#39;s preferences that, in turn, provide a significant indicator for selection of interest compatible candidate matches. The inherent flexibility and breadth of available user vocabulary however impedes the effective and efficient use of such freeform text in the pairing or relationship building process outside of finding literal text matches in user profiles. 
     The Applicants have found an analysis technique permitting the autonomous analysis of freeform text found in the profiles of relationship building or pairing Website users. The process commences by optionally preprocessing the freeform text data to remove non-letter characters and stop words (e.g., words such as “I” “the” “a” and others that provide no information regarding the user&#39;s interests) from the freeform text provided by the user. In some implementations, some or all of the words included in the freeform text may be stemmed, for example using a Porter stemming technique. After any preprocessing, the process autonomously creates a defined number of user interest groups based on the content and structure of freeform text entries provided by a user population. Words are classified or categorized within each user interest group based upon a measure of their “indicativeness” or “representativeness” of the user interest group as a whole. Thus, for a user interest group such as “outdoor team sports” words such as “baseball,” “football,” and “soccer” may be highly ranked (all are outdoor team sports); “basketball” and “ice hockey” may be intermediately ranked (all are indoor team sports); and “chess” may be low ranked (neither a team nor outdoor sport). 
     Advantageously, such a user interest group model is possible for each language and/or each dialect of a language. Additionally, since the model is generated based on both the words and their relationship, the model tends to reflect local culture, vernacular, and mores. Thus, the evaluation system is equally implementable and effective in finding candidate matches that reflect local language and culture around the world. Furthermore, even commonly misspelled words will be included in the user interest group model by their presence among other freeform text which provides context for the inclusion of the misspelled words at an appropriate location within the user interest groups. 
     Based at least in part on the word distribution within each user interest group, the freeform text inputted by each user is evaluated to generate a user interest group membership vector for each user included in the user population. The user interest group membership vector is an n-component vector, where each of the “n” components corresponds to a value indicative of the respective user&#39;s interest in a corresponding one of the “n” number of user interest groups. Each user interest group membership vector therefore characterizes the interests of the respective user as expressed by the language in the respective user&#39;s freeform text. Various methods may then be used to analyze at least one aspect of compatibility between users based on the interests expressed in the user&#39;s freeform text provided to the service provider. One aspect of compatibility between users may be measured by the degree of similarity and/or distance between user interest group vectors for various candidate user pairings. Such analysis methods may include determining the cosine similarity or the Euclidean distance between two user interest group vectors. 
     In addition to assessing one or more aspects of compatibility between users based on their respective user interest group membership vectors, such vectors may be used in other, surprisingly, beneficial applications. In a first instance, a user may provide a freeform text entry of interests they would find “desirable” or “ideal” in candidate matches. Using this information, the evaluation system can generate a “hypothetical” user interest group membership vector using the ideal interests entered by the user which is then used to provide candidate matches to the user. In a second instance, a single hypothetical user interest group membership vector may be generated for a group of users. To assess compatibility of new users with the group, the evaluation system may assess the hypothetical user interest group membership vector and the user&#39;s user interest group membership vector. In a third instance, a user may identify another user whom they find has desirable or ideal interests. The evaluation system can use the user interest group membership vector associated with identified user to identify and provide candidate matches to the user. In a fourth instance, a user may express an interest in another user by their actions such as hovering over another user&#39;s picture or repeatedly visiting another user&#39;s profile. The evaluation system can use the user interest group membership vector associated with the other user to identify and provide candidate matches to the user. 
     A method of operation in an evaluation system, the evaluation system including at least one processor and at least one nontransitory processor-readable medium that stores at least one of processor-executable instructions or data, the at least one nontransitory processor-readable medium communicatively coupled to the at least one processor, the method may be summarized as including: deriving by the at least one processor a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text, where the number of interest groups in the set of user interest groups is less than a total number of user interests in the plurality of user interests specified by the plurality of users; for each of a number of the users, determining by the at least one processor a respective interest group membership vector based on respective user interests specified by the respective user, the interest group membership vector having one or more elements which represent a partial membership in respective ones of one or more of the interest groups; and assessing by the at least one processor at least one of the users based at least in part on at least one of the interest group membership vectors. 
     Assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include comparing a respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users. Comparing a respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users may include determining a distance between the respective interest group membership vector for the first one of the users and the respective interest group membership vector for the at least one other one of the users. Comparing a respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users may include determining a similarity between the respective interest group membership vector for the first one of the users and the respective interest group membership vector for the at least one other one of the users. 
     The method of operation in an evaluation system may further include, for at least one of the interest group membership vectors, normalizing by the at least one processor the respective interest group membership vector, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing at least a first one of the users based at least in part on the normalized respective interest group membership vector for the first one of the users. 
     For each of a number of the users, determining a respective interest group membership vector based on respective user interests specified by the respective user may include determining a respective interest group membership vector for each active user associated with a first defined language or first defined dialect of a language. 
     The method of operation in an evaluation system may further include: receiving the plurality of user interests specified by the plurality of users as freeform text; and preprocessing the received plurality of user interests specified by the plurality of users as freeform text. 
     Preprocessing the received plurality of user interests specified by the plurality of users as freeform text may include at least one of: removing by the at least one processor characters that are not valid letters in a valid language from the received plurality of user interests specified by the plurality of users as freeform text; removing by the at least one processor stop words from the received plurality of user interests specified by the plurality of users as freeform text; or performing by the at least one processor stemming on the received plurality of user interests specified by the plurality of users as freeform text. Deriving a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may include deriving the set of interest groups from the preprocessed plurality of user interests specified by the plurality of users, which user interests may include at least one incorrect spelling of a user interest, and at least one pair of alternative names for a given user interest. Deriving a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may include generating a generative natural language processing model based at least in part on the plurality of user interests specified by the plurality of users as freeform text. Generating a generative natural language processing model may include preforming a Latent Dirichlet Allocation on preprocessed user interests specified by the plurality of users as freeform text. Each of the users may provide a respective set of user interest words in freeform text and deriving a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may include computing for a number of interest words a respective interest word frequency-inverse set of user interest words frequency. 
     The method of operation in an evaluation system may further include: receiving a set of self-specified user interests provided by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between the respective interest group membership vector of the first one of the users and respective interest group membership vectors of the other ones of the users. 
     The method of operation in an evaluation system may further include: receiving a set of user interests desired by a first one of the users in another one of the users; generating a hypothetical interest group membership vector based on the received set of user interests desired by the first one of the users in another one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between the hypothetical interest group membership vector and respective interest group membership vectors of the other ones of the users. 
     The method of operation in an evaluation system may further include: receiving an identification of one user by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between a respective interest group membership vector of the identified one of the users and respective interest group membership vectors of other ones of the users. 
     Receiving an identification of one user by a first one of the users may include receiving identification of at least one user who has at least one trait, other than user interests, which disqualifies the respective at least one user as a match for the first one of the users. 
     The method of operation in an evaluation system may further include: detecting an interest in one user by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between a respective interest group membership vector of the one of the users in which the interest was detected and respective interest group membership vectors of other ones of the users. 
     The method of operation in an evaluation system may further include: selecting by the at least one processor a first subset of the users based on traits other than user interests, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing a compatibility of the users of the first subset of users with a first one of the users based on a respective interest group membership vector for the first one of the users with the respective interest group membership vector for the other one of the users in the first subset of the users. 
     The method of operation in an evaluation system may further include: determining by the at least one processor at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include comparing a respective interest group membership vector for a first user with the at least one hypothetical interest group membership vector. 
     Determining at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may include determining at least one of a mean or median of the respective interest group membership vectors of the set of users. Determining at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may include determining the at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a set of users that each have at least two demographic attributes in common. Determining at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may include determining the at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a set of users that each have a geographic locale in common. 
     The method of operation in an evaluation system may further include: determining a relative distance between the respective interest group membership vector of the first user and the at least one hypothetical interest group membership vector; and providing an indication of the determined relative distance. 
     The method of operation in an evaluation system may further include: identifying at least one other set of users with which the first user is more compatible based on the respective interest group membership vector of the first user and at least one hypothetical interest group membership vector of the at least one other set of users; and providing an indication of the determined other set of users with which the first user is more compatible. 
     The method of operation in an evaluation system may further include: selecting the set of users based on at least a first characteristic that is common to the users of the first set; and selecting the at least one other set of users based on at least a second characteristic that is common to the users of the second set and not common to the users of the first set. 
     The method of operation in an evaluation system may further include: selecting the set of users based on at least a first geographical locale that is common to the users of the first set; and selecting the at least one other set of users based on at least a second geographic locale that is common to the users of the second set and not common to the users of the first set. 
     The method of operation in an evaluation system may further include: selecting a first subset of the users based on traits other than user interests, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors includes assessing a dissimilarity of the users of the first subset of users with a first one of the users based on a respective interest group membership vector for the first one of the users with the respective interest group membership vector for the other one of the users in the first subset of the users. 
     The method of operation in an evaluation system may further include determining at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors includes displaying at least one interest-based characteristic of the group identified at least in part via the hypothetical interest group membership vector. 
     The method of operation in an evaluation system may further include: determining a first hypothetical interest group membership vector based on the respective interest group membership vectors of a first plurality of the users. The method of operation may additionally include determining a second hypothetical interest group membership vector based on the respective interest group membership vectors of a second plurality of the users. In such methods assessing at least one of the users based at least in part on at least one of the interest group membership vectors includes determining at least one of a similarity or a dissimilarity between the first plurality of the users the second plurality of the users by comparing the first hypothetical interest group membership vector with the second hypothetical group membership vector. 
     The method of operation in an evaluation system may further include: determining the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait. The method of operation in an evaluation system may further include determining a second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least a second non-interest based trait that is different than the second non-interest based trait. In such methods assessing at least one of the users based at least in part on at least one of the interest group membership vectors includes comparing the interests of the first plurality of the users as provided by the first hypothetical interest group membership vector with the interests of the second plurality of the users as provided by the second hypothetical group membership vector. In such methods determining the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait includes determining the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait that includes a defined first geographic location. In such methods determining the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least a second non-interest based trait includes determining the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share a third non-interest based trait that includes a defined second geographic location. In such methods, the first plurality of the users and the second plurality of the users share at least a third non-interest based trait and determining by the at least one processor the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait and a second non-interest based trait includes determining by the at least one processor the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait that includes a defined first geographic location and a third non-interest based trait. In such methods, the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait and a third non-interest based trait includes determining by the at least one processor the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait that includes a defined second geographic location and a third non-interest based trait. 
     An evaluation system may be summarized as including: at least one processor; and at least one nontransitory processor-readable medium communicatively coupled to the at least one processor that stores at least one of processor-executable instructions or data that when executed by the at least one processor cause the at least one processor to: derive a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text, where the number of interest groups in the set of user interest groups is less than a total number of user interests in the plurality of user interests specified by the plurality of users; for each of a number of the users, determine a respective interest group membership vector based on respective user interests specified by the respective user the interest group membership vector having one or more elements which represent a partial membership in respective ones of one or more of the interest groups; and assess at least one of the users based at least in part on at least one of the interest group membership vectors. 
     The processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors may further cause the at least one processor to: compare a respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users. The processor-executable instructions that cause the at least one processor to compare the respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users may further cause the at least one processor to: determine a distance between the respective interest group membership vector for the first one of the users and the respective interest group membership vector for the at least one other one of the users. The processor-executable instructions that cause the at least one processor to compare a respective interest group membership vector for a first one of the users with the respective interest group membership vector for at least one other one of the users may further cause the at least one processor to: determine a similarity between the respective interest group membership vector for the first one of the users and the respective interest group membership vector for the at least one other one of the users. The processor-executable instructions may further cause the at least one processor to: normalize the respective interest group membership vector for at least one of the interest group membership vectors; and wherein the processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors may further cause the at least one processor to: assess at least a first one of the users based at least in part on the normalized respective interest group membership vector for the first one of the users. The processor-executable instructions that cause the at least one processor to determine a respective interest group membership vector based on respective user interests specified by the respective user may further cause the at least one processor to: determine a respective interest group membership vector for each active user associated with a first defined language or first defined dialect of a language. The processor-executable instructions may further cause the at least one processor to: receive the plurality of user interests specified by the plurality of users as freeform text; and preprocess the received plurality of user interests specified by the plurality of users as freeform text. The processor-executable instructions that cause the at least one processor to preprocess the received plurality of user interests specified by the plurality of users as freeform text may further cause the at least one processor to at least one of: remove characters that are not valid letters in a valid language from the received plurality of user interests specified by the plurality of users as freeform text; remove stop words from the received plurality of user interests specified by the plurality of users as freeform text; or perform stemming on the received plurality of user interests specified by the plurality of users as freeform text. The processor-executable instructions that cause the at least one processor to derive a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may further cause the at least one processor to: derive the set of interest groups from the preprocessed plurality of user interests specified by the plurality of users, which user interests may include at least one incorrect spelling of a user interest, and at least one pair of alternative names for a given user interest. The processor-executable instructions that cause the at least one processor to derive a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may further cause the at least one processor to: generate a generative natural language processing model based at least in part on the plurality of user interests specified by the plurality of users as freeform text. The processor-executable instructions that cause the at least one processor to generate a generative natural language processing model may further cause the at least one processor to: perform a Latent Dirichlet Allocation on preprocessed user interests specified by the plurality of users as freeform text. The processor-executable instructions that cause the at least one processor to derive a set of user interest groups from a plurality of user interests specified by a plurality of users as freeform text may further cause the at least one processor to: compute for a number of interest words, provided by each of the users provides a respective set of user interest words in freeform text, a respective interest word frequency-inverse set of user interest words frequency. The processor-executable instructions may further cause the at least one processor to: receive a set of self-specified user interests provided by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between the respective interest group membership vector of the first one of the users and respective interest group membership vectors of the other ones of the users. The processor-executable instructions may further cause the at least one processor to: receive a set of user interests desired by a first one of the users in another one of the users; generate a hypothetical interest group membership vector based on the received set of user interests desired by the first one of the users in another one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between the hypothetical interest group membership vector and respective interest group membership vectors of the other ones of the users. The processor-executable instructions may further cause the at least one processor to: receive an identification of one user by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between a respective interest group membership vector of the identified one of the users and respective interest group membership vectors of other ones of the users. The processor-executable instructions that cause the at least one processor to receive an identification of one user by a first one of the users may further cause the at least one processor to: receive identification of at least one user who has at least one trait, other than user interests, which disqualifies the respective at least one user as a match for the first one of the users. The processor-executable instructions may further cause the at least one processor to: detect an interest in one user by a first one of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing potential matches with other ones of the users based on an assessment in commonality or differences between a respective interest group membership vector of the one of the users in which the interest was detected and respective interest group membership vectors of other ones of the users. The processor-executable instructions may further cause the at least one processor to: select by the at least one processor a first subset of the users based on traits other than user interests, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include assessing a compatibility of the users of the first subset of users with a first one of the users based on a respective interest group membership vector for the first one of the users with the respective interest group membership vector for the other one of the users in the first subset of the users. The processor-executable instructions may further cause the at least one processor to: determine by the at least one processor at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users, and wherein assessing at least one of the users based at least in part on at least one of the interest group membership vectors may include comparing a respective interest group membership vector for a first user with the at least one hypothetical interest group membership vector. The processor-executable instructions that cause the at least one processor to determine at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may further cause the at least one processor to: determine at least one of a mean or median of the respective interest group membership vectors of the set of users. The processor-executable instructions that cause the at least one processor to determine at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may further cause the at least one processor to: determine the at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a set of users that each have at least two demographic attributes in common. The processor-executable instructions that cause the at least one processor to determine at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users may further cause the at least one processor to: determine the at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a set of users that each have a geographic locale in common. The processor-executable instructions may further cause the at least one processor to: determine a relative distance between the respective interest group membership vector of the first user and the at least one hypothetical interest group membership vector; and provide an indication of the determined relative distance. The processor-executable instructions may further cause the at least one processor to: identify at least one other set of users with which the first user is more compatible based on the respective interest group membership vector of the first user and at least one hypothetical interest group membership vector of the at least one other set of users; and provide an indication of the determined other set of users with which the first user is more compatible. The processor-executable instructions may further cause the at least one processor to: select the set of users based on at least a first characteristic that is common to the users of the first set; and select the at least one other set of users based on at least a second characteristic that is common to the users of the second set and not common to the users of the first set. The processor-executable instructions may further cause the at least one processor to: select the set of users based on at least a first geographical locale that is common to the users of the first set; and select the at least one other set of users based on at least a second geographic locale that is common to the users of the second set and not common to the users of the first set. 
     The processor-executable instructions further cause the at least one processor to: select a first subset of the users based on traits other than user interests; wherein the processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors, further cause the at least one processor to: assess a dissimilarity of the users of the first subset of users with a first one of the users based on a respective interest group membership vector for the first one of the users with the respective interest group membership vector for the other one of the users in the first subset of the users. The processor-executable instructions further cause the at least one processor to: determine at least one hypothetical interest group membership vector based on the respective interest group membership vectors of a plurality of the users; and wherein the processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors, further cause the at least one processor to: display at least one interest-based characteristic of the group identified at least in part via the hypothetical interest group membership vector. 
     The processor-executable instructions further cause the at least one processor to: determine a first hypothetical interest group membership vector based on the respective interest group membership vectors of a first plurality of the users; determine a second hypothetical interest group membership vector based on the respective interest group membership vectors of a second plurality of the users; and wherein the processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors, further cause the at least one processor to: assess at least one of a similarity or a dissimilarity between the first plurality of the users the second plurality of the users by comparing the first hypothetical interest group membership vector with the second hypothetical group membership vector. 
     The processor-executable instructions further cause the at least one processor to: determine the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based profile attribute; determine a second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least a second non-interest based profile attribute that is different than the first non-interest based profile attribute; and wherein the processor-executable instructions that cause the at least one processor to assess at least one of the users based at least in part on at least one of the interest group membership vectors, further cause the at least one processor to: assess the interests of the first plurality of the users as provided by the first hypothetical interest group membership vector with the interests of the second plurality of the users as provided by the second hypothetical group membership vector. 
     The processor-executable instructions that cause the at least one processor to determine the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait and a second non-interest based trait, further cause the at least one processor to; determine the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait that includes a defined first geographic location; and wherein the processor-executable instructions that cause the at least one processor to determine by the at least one processor the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait further cause the at least one processor to; determine the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait that includes a defined second geographic location. 
     The processor-executable instructions that cause the at least one processor to determine by the at least one processor the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait and a second non-interest based trait further cause the at least one processor to determine by the at least one processor the first hypothetical interest group membership vector based on the respective interest group membership vectors of the first plurality of the users who share at least a first non-interest based trait that includes a defined first geographic location and a third non-interest based trait; and wherein the processor-executable instructions that cause the at least one processor to determine the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait and a third non-interest based trait, further cause the at least one processor to: determine the second hypothetical interest group membership vector based on the respective interest group membership vectors of the second plurality of the users who share at least the second non-interest based trait that includes a defined second geographic location and the third non-interest based trait. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       In the drawings, identical reference numbers identify similar elements or acts. The sizes and relative positions of elements in the drawings are not necessarily drawn to scale. For example, the shapes of various elements and angles are not drawn to scale, and some of these elements are arbitrarily enlarged and positioned to improve drawing legibility. Further, the particular shapes of the elements as drawn are not intended to convey any information regarding the actual shape of the particular elements, and have been solely selected for ease of recognition in the drawings. 
         FIG. 1  is a schematic view of a user evaluation environment that includes an evaluation system useful for generating user interest group membership vectors for each user in a number of users and evaluating pairings between two or more users in the number of users using the user interest group membership vectors, according to one illustrated embodiment. 
         FIG. 2  is a plate diagram of an example method of generating a user interest topic schema using a generative Natural Language Processing (NLP) method, according to 
         FIG. 3  is a functional block diagram of an evaluation system that matches two or more users in a number of users based on their respective user interest group membership vectors, according to one illustrated embodiment. 
         FIG. 4  is a flow diagram showing a high-level method of forming user interest groups using freeform text supplied by a number of users, generating user interest group membership vectors for each of the number of users, and assessing users via the user interest group membership vector, according to one illustrated embodiment. 
         FIG. 5  is a flow diagram showing a high-level method of identifying candidate user matches by comparing the user interest group membership vectors for two or more users, according to one illustrated embodiment. 
         FIG. 6  is a flow diagram showing a high-level method of screening a number of users to identify a subset of potential or candidate matches for a user using one or more non-interest based selection criteria, according to one illustrated embodiment. 
         FIG. 7  is a flow diagram showing a high-level method of identifying candidate user matches by comparing normalized user interest group membership vectors for two or more users, according to one illustrated embodiment. 
         FIG. 8  is a flow diagram showing a high-level method of preprocessing freeform text entries provided by users and determining the user interest groups using the preprocessed freeform text entries provided by the users, according to one illustrated embodiment. 
         FIG. 9  is a flow diagram showing a high-level method of receiving a specified set of user defined interests, generating a user interest group membership vector for the user and evaluating candidate matches with other users by comparing the generated user interest group membership vector to other the user interest group membership vectors associated with the other users, according to one illustrated embodiment. 
         FIG. 10  is a flow diagram showing a high-level method of assessing candidate users in a number of users by comparing the user interest group membership vectors for the candidate users with a user interest group membership vector generated using desired interests provided by a first user, according to one illustrated embodiment. 
         FIG. 11  is a flow diagram showing a high-level method of assessing candidate users in a number of users by comparing the user interest group membership vectors for the candidate users with a user interest group membership vector associated with a second user chosen by a first user, according to one illustrated embodiment. 
         FIG. 12  is a flow diagram showing a high-level method of assessing candidate users in a number of users by comparing the user interest group membership vectors for the candidate users with a user interest group membership vector associated with a second user that is autonomously selected based at least in part on an action by a first user, according to one illustrated embodiment. 
         FIG. 13  is a flow diagram showing a high-level method of assessing a compatibility of the users included in a first subset of users with a first one of the users, according to one illustrated embodiment. 
         FIG. 14  is a flow diagram showing a high-level method of assessing the compatibility of a first user with a first plurality of users by comparing the first user&#39;s user interest group membership vector with a hypothetical user interest group membership vector for the first plurality of users, according to one illustrated embodiment. 
         FIG. 15  is a flow diagram showing a high-level method of identifying a second plurality of users demonstrating a greater compatibility with the first user based on a hypothetical user interest group membership vector representative of the individual users included in the second group and a first user&#39;s user group membership vector, according to one illustrated embodiment. 
         FIG. 16  is a flow diagram showing a high-level method of assessing a dissimilarity between the users included in a first subset of users and a first one of the users, according to one illustrated embodiment. 
         FIG. 17  is a flow diagram showing a high-level method of comparing a first plurality of the users with a second plurality of the users based on respective hypothetical interest group membership vectors determined for each of the first plurality of the users and the second plurality of the users, according to one illustrated embodiment. 
         FIG. 18  is a flow diagram showing a high-level method of generating a hypothetical interest group membership vector for a plurality of the users and displaying at least one interest based trait associated with the plurality of the users based on the hypothetical interest group membership vector, according to one illustrated embodiment. 
         FIG. 19  is a flow diagram showing a high-level method of comparing a first plurality of the users who share a first non-interest based trait and a second non-interest based trait with a second plurality of the users who share the first non-interest based trait and a third non-interest based trait based on respective hypothetical interest group membership vectors determined for each of the first plurality of the users and the second plurality of the users, according to one illustrated embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, certain specific details are set forth in order to provide a thorough understanding of various disclosed embodiments. However, one skilled in the relevant art will recognize that embodiments may be practiced without one or more of these specific details, or with other methods, components, materials, etc. Operational level details, programming, and statistical underpinnings of Latent Dirichlet Allocation (“LDA”) and other similar generative natural language processing models are well understood by those of skill in the relevant arts and are readily accessible and will not be addressed in depth, described or shown in detail herein to avoid obscuring the presented embodiments. Additionally, construction, specification and operational level details of standard electronic components such as processors, nontransitory storage media and/or devices, input/output interfaces, and wired and wireless networking are also known to those of skill in the relevant arts and are also neither described nor shown in detail herein. 
     Unless the context requires otherwise, throughout the specification and claims which follow, the word “comprise” and variations thereof, such as, “comprises” and “comprising” are to be construed in an open, inclusive sense, that is as “including, but not limited to.” 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. 
     As used herein users referred to by the descriptive terms “match,” “matches,” “potential candidate,” “potential candidates,” “potential candidate match,” and “potential candidate matches” collectively refer to a set of users that includes any number of users having user interest group membership vectors that fall within a defined acceptable range, distance, or other measurement attribute of a defined user interest group membership vector. 
     As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. It should also be noted that the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise. 
     The headings and Abstract of the Disclosure provided herein are for convenience only and do not interpret the scope or meaning of the embodiments. 
       FIG. 1  shows an evaluation system environment  100  in which evaluation system  110  that includes a model generation subsystem  112  and a run-time subsystem  114  receives user profile data  104   a - 104   n  (collectively “profile data  104 ” from a respective number of users  102   a - 102   n  (collectively, “users  102 ”). In some implementations, the number of users  102   a - 102   n  may include an entire user population. In other instances, the number of users  102   a - 102   n  may include a subset of users manually or autonomously selected from a larger user population. For example, the number of users  102   a - 102   n  may include a subset of potential or candidate matches chosen by the service provider  108  using one or more non-interest based matching criteria (e.g., gender, orientation, geographic location, age, income, education, and the like). 
     The model generation subsystem  112  receives the user profile data  104   a - 104   n  and uses freeform text  106   a - 106   n  present in the user profiles to generate a user interest group model  142 . The run-time system  114  receives the user interest group model  142  and, using the freeform text  106   a - 106   n  included in the user profiles  104   a - 104   n , determines a respective user interest group membership vector  152  for each of the users  102   a - 102   n . The run-time system  114  uses the user interest group membership vectors  152   a - 152   n  to identify potential or candidate user matches within the number of users  102   a - 102   n . The service provider  108  can communicate the potential or candidate matches to each of the respective users  102   a - 102   n.    
     Within the model generation subsystem  112 , freeform text  106  provided by a user  102  in the profile data  104  is preprocessed using one or more preprocessing modules  120 . At least a portion of the freeform text provided by the user  102  may include information indicative of the user&#39;s personal interests and/or interests that the user  102  would find desirable in a partner. 
     At least a portion of the preprocessed freeform text  106  is forwarded to a user interest term matrix generator module  130 . The user interest term matrix generator module  130  generates a user interest term matrix  132 . The user interest term matrix  132  shows a distribution or frequency of appearance of words or terms  134   a - 134   m  (collectively “terms  134 ”) used within all or a portion of the freeform text  106  included in each of the use profiles  104   a - 104   n.    
     Some or all of the data included in the user interest term matrix  132  is provided to the user interest group generation module  140 . Using a natural language generative model such as Latent Dirichlet Allocation, the user interest group generation module  140  produces a user interest group model  142 . The user interest group model  142  includes a defined number of user interest groups  144   a - 144   n  (collectively “user interest groups  144 ”), each of which includes a number of words  146   a - 146   n  (collectively, “words  146 ”) logically associated with the respective user interest group  144 . The words  146  associated with a particular user interest group  144  may have a logically associated value indicative of the degree or strength of the association between the respective word  146  and the user interest group  144 . 
     Some or all of the data included in the user interest group model  142  is provided to the user interest analysis module  150 . Based on the freeform text  106   a - 106   n  included in each user&#39;s profile  104   a - 104   n , the user interest analysis module  150  generates a respective user interest group membership vector  152   a - 152   n  for each user  102   a - 102   n . The user interest group membership vector  152  includes a number of components, values, or elements  156   a - 156   n  (collectively, “elements  156 ”), each of which corresponds to one of the number of user interest groups  144   a - 144   n . Thus, each user  102  is associated by the evaluation system  110  with a respective user interest group membership vector  152  that characterizes the interests of the user  102  in each of the user interest groups  144  as a value indicative of the strength of the respective user&#39;s interest in the particular user interest group  144 . 
     The user interest group membership vector  152  is provided to the user interest matching module  160 . Based on the values in a particular user&#39;s  102  user interest group membership vector  152 , candidate matches for the particular user  102  are determined based upon a degree of similarity and/or a degree of difference between the user&#39;s user interest group membership vector  152  and the candidate matching user&#39;s user interest group membership vector  152 . 
     Although the evaluation system  110  will be discussed in the form of subsystems  112 ,  114  each containing a number of discrete “modules”  120 ,  130 ,  140 ,  150 ,  160  for clarity and ease of discussion, those of skill in the relevant arts will readily appreciate that any number of subsystems and/or modules may be embodied in any number of processors and/or computing devices. Thus all of the discussed modules may be incorporated into processor-executable code executed by a single computing device. Additionally, the above evaluation system  110  will be discussed in terms of an illustrative relationship building or “dating” Website. Those of ordinary skill in the relevant arts will readily appreciate the applicability of such evaluation systems  110  in providing matches between parties in different circumstances such as, supplier/consumer, employer/employee, and others. 
     Relationship building Websites typically require new users  102  to register and generate a user profile  104  that is stored by the service provider and used to locate potential matches from among other registered users of the Website. At times, users  102  will generate profiles  104  that include freeform text  106 . Such freeform text may be used to provide information or data to the service provider that is not easily, conveniently, or accurately reducible to binary selection criteria such as check-boxes or radio buttons. The information contained in such freeform text  106  is usually indicative of the interests, activities, or other information characteristic of the user  102 , at times on multiple levels. For example, the literal terms provided by the user as freeform text each provide insight into the respective user&#39;s interests. On a deeper level, the relationship between the words provides a conceptual fabric underpinning the respective user&#39;s interest or interests. 
     In one example, a list of words included in freeform text  106   a - 106   n  provided by users  102   a - 102   n  may include a number of terms having similar or identical meanings such as “sail,” “sailing,” “boating,” and “cruising” all of which are descriptive of an interest in an activity involving watercraft. In another example, a list of words included in freeform text  106  provided by a first user  102   a  may include “hunting,” “fishing,” and “camping,” which may be considered as generally indicating the first user&#39;s interest in back-country outdoor activities. A list of words included in freeform text  106  provided by a second user  102   b  may include “hiking,” “spelunking,” and “rock climbing,” which may be considered as generally indicating the second user&#39;s interest in back-country outdoor activities. In the absence of a common word or phrase appearing in the freeform text provided by each user, many matching or pairing algorithms would fail to recognize the potential matching of the first user with the second user based on their mutual interests in back-country outdoor activities. The use of an evaluation system  110  capable of generating user interest group membership vectors  152  including values associated with defined number of user interest groups  144  has been found to advantageously recognize such candidate pairings even in the absence of common terms, or in the presence of misspelled terms and/or idiomatic differences in a common language. By recognizing such, heretofore unrecognized, pairings within a large number of users  102   a - 102   n , the quality of service provided by the service provider is improved and the perceived value to users is increased commensurately. 
     At least the freeform text  106  provided by each user  102   a - 102   n  in their respective, logically associated, user profile  104   a - 104   n  is communicated from the service provider&#39;s host Website  108  to the evaluation system  110 , for example via one or more networks  109 . In some instances, within the evaluation system  110 , the preprocessing module  120  optionally receives at least a portion of the freeform text  106   a - 106   n  from at least some of the user profiles  104   a - 104   n.    
     The preprocessing module  120  alters and/or edits the freeform text  106  provided by the users  102  preparatory to generating the user interest term matrix  132 . In some instances, such alteration and/or editing of the freeform text  106  has been found to improve the overall performance of the user interest group model generator  140 . The preprocessing module  120  removes non-letter characters from the freeform text  106 . The preprocessing module  120  also removes “stop words” from the freeform text  106 . Stop words include words such as “a,” “the,” “I,” “me,” and others that provide no indication of a user interest and are included in a defined dictionary. 
     In some implementations, the preprocessing module  120  may also optionally stem the words in the freeform text  106 . Such stemming may be performed using a Porter stemmer. An example Porter stemmer is described in detail in the “The Porter Stemming Algorithm: Then and Now” (Willett, P. (2006) The Porter stemming algorithm: then and now. Program: electronic library and information systems, 40 (3). pp. 219-223) which to the extent it is not contradictory to the information contained herein is incorporated by reference as if reproduced in its entirety herein. A word stemmer stems different variations of a word back to a single word stem, for example “movie” and “movies” would both be stemmed to “movi.” A word stemmer thus serves to reduce the number of words included in a vocabulary without reducing the overall richness or variety of concepts conveyed by the words included in the vocabulary. 
     The preprocessing module  120  then computes the term frequency-inverse document frequency (tf-idf) for each of the words included in the freeform text  106  of the user profiles  104   a - 104   n . The tf-idf is given by:
 
 tf ( t,d )× idf ( t,D )  (1)
 
     Where: tf(t,d)=1 if the word t appears in document d,  0  otherwise; and 
     
       
         
           
             
               
                 
                   
                     idf 
                     ⁡ 
                     
                       ( 
                       
                         t 
                         , 
                         D 
                       
                       ) 
                     
                   
                   = 
                   
                     log 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       N 
                       
                          
                         
                           { 
                           
                             d 
                             ∈ 
                             
                               D 
                               : 
                               
                                 t 
                                 ∈ 
                                 d 
                               
                             
                           
                           } 
                         
                          
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     Where: N=total number of user profiles
         D=number of documents in which word t appears       

     The preprocessing module  120  then discards those words included in the freeform text  106   a - 106   n  having a tf-idf value below a defined threshold. Words having a tf-idf value below a defined threshold appear in a high percentage of the freeform text  106   a - 106   n  provided by the users  102   a - 102   n . The ubiquity of such words in the freeform text  106  tends to provide limited information gain, thus the elimination of such words generally reduces system overhead requirements and improves the quality of user interest group determination. After determining the tf-idf for each word, blank user profiles  104  (i.e., those user profiles  104  having a freeform text entry in which every word has been discarded based on the tf-idf) are discarded. 
     The edited freeform text  106  is passed from the preprocessing module  120  to the user interest term matrix generator module  130 . The user interest term matrix generator module  130  generates a user interest term matrix  132  that indicates the frequency or mere occurrence of use of words  134   a - 134   m  in the freeform text  106   a - 106   n  portions of some or all of the user profiles  104   a - 104   n.    
     The user interest term matrix  132  is passed from the user interest term matrix generator module  130  to the user interest group generator module  140 . The user interest group generator module  140  employs a generative Natural Language Processing (NLP) model using a hierarchical Bayesian inference to discover latent similarities between passages of freeform text  106   a - 106   n  included in any number of user profiles  104   a - 104   n . In at least some instances, the generative NLP model includes Latent Dirichlet Allocation (“LDA”) although other generative NLP models may be substituted. The generative NLP model discovers relations between passages of freeform text  106   a - 106   n  and clusters such relations into a defined number of user interest groups  144   a - 144   n . Advantageously, the relationships discovered by the NLP model are not defined but instead are organic to the freeform text  106   a - 106   n  provided by the users  102   a - 102   n.    
     The user interest group model  142  is determined based at least in part on the, the vocabulary (“V”) of words  134   a - 134   m , the set (“M”) of freeform text  106   a - 106   n , and a defined number (“K”) of user interest groups  144   a - 144   n . Using a LDA model as an illustrative example, let:
         α=vector parameter of the Dirichlet prior on the per-document user interest group distribution   β=vector parameter of the Dirichlet prior on the per-user interest group word distribution   θ i =user interest group distribution for document i   φ k =word distribution for topic k   z ij =user interest group for the j th  word in document i   w ij =specific j th  word in document i       

     The user interest group generator module  140  attempts to infer both the defined number of user interest groups  144   a - 144   n  (θ i ) and the word  134   a - 134   m  distribution with each of the defined number of user interest groups  144   a - 144   n  (φ k ). With reference to the plate notation graphical model depicted in  FIG. 2 , the total probability may be expressed as:
 
 p ( {right arrow over (w)},{right arrow over (z)} ,{right arrow over (θ)},{right arrow over (φ)},α,β)=Π i=1   K   p (φ i ,β)Π j=1   M   p (θ j ,α)Π t=1   N     j     p ( z   j,t |θ j ) p ( w   j,t   |φZ   j,t )  (3)
 
Since the occurrence of words in the freeform text  106   a - 106   n  is known, the word  134   a - 134   m  distribution within the freeform text  106   a - 106   n  may be determined and the user interest group  144   a - 144   n  inferred using the following equation which may be referred to as the posterior distribution:
 
     
       
         
           
             
               
                 
                   
                     p 
                     ⁡ 
                     
                       ( 
                       
                         
                           θ 
                           → 
                         
                         , 
                         
                           ϕ 
                           → 
                         
                         , 
                         
                           
                             z 
                             → 
                           
                           | 
                           
                             w 
                             → 
                           
                         
                         , 
                         α 
                         , 
                         β 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           θ 
                           , 
                           ϕ 
                           , 
                           z 
                           , 
                           
                             w 
                             | 
                             α 
                           
                           , 
                           β 
                         
                         ) 
                       
                     
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           
                             w 
                             | 
                             α 
                           
                           , 
                           β 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     The denominator of equation (4) is intractable to exact determination as it involves multiple integrals that are not separable or expressible in closed form. Consequently, approximation techniques such as Variational Expectation Maximization and Collapsed Gibbs Sampling may be used to approximate the value of the denominator in equation (4) and determine the probability value p({right arrow over (θ)}, {right arrow over (φ)}, {right arrow over (z)}|{right arrow over (w)}, α, β). The resultant value provides a probability that a particular word  134  is related to a particular user interest group  144 . Each word  134   a - 134   m  extracted from the freeform text  106   a - 106   n  provided by users  102   a - 102   n  is similarly evaluated for each user interest group. The resultant user interest group model  142  is therefore an “M” word by “K” user interest group matrix of values indicative of a probability that the m th  word is related to the k th  user interest group. 
     The probability distribution is generally indicative of the relationship between the respective word  134  and the respective user interest group  144 . Thus, the user interest group generator module  140  may determine words such as “ski,” “snowboard,” and “snowshoe” share a close relationship due to the frequency of their appearance together in the freeform text  106   a - 106   n  provided by users. In such an instance, the words “ski,” “snowboard,” and “snowshoe” may be associated with higher probability values in a first user interest group  144   a  that may be labeled “outdoor winter sports,” and may be associated with significantly lower probability values in a second user interest group  144   b  that may be labeled “cooking.” 
     The defined number of user interest groups  144   a - 144   n  is determined by the service provide based on empirical experience and observation. Too few user interest groups  144   a - 144   n  may result in an overly large number of marginal candidate user matches. For example, a “sports” user interest group may include every sport related word included in the freeform text  106   a - 106   n  provided by users  102   a - 102   n . Such a broad interest group  144  would result in the essentially meaningless pairing of a user  102   a  who has indicated an interest in football with every other user who has indicated interests in golf, tennis, badminton, and volleyball. In contrast, a “badminton” user interest group  144  may result in a low number of very closely aligned candidate user matches between users who have included words such as “badminton” or “poona” in their freeform text  106  while ignoring other potentially sound candidate matches who have included words such as “tennis” and “table tennis” (i.e., related racquet/net sports) in their freeform text  106 . 
     The language used in the freeform text  106   a - 106   n  provided by users  102   a - 102   n  will not remain static over time. As new words enter the vernacular and old words depart, the changes are reflected in the freeform text  106 . To accommodate such linguistic changes, periodically, intermittently, or from time-to-time, the evaluation system  110  may pull or the service provider  108  may push some or all of the user profiles  104   a - 104   n  to the model generation subsystem  112 . The model generation subsystem  112  will reprocess the freeform text  106  included in the user profiles in the manner discussed above to generate an updated user interest group model  142  for use by the run-time subsystem  114 . 
     Advantageously, the user interest group generator module  140  accommodates the use of commonly misspelled words. For example, if the word “swimming” is frequently misspelled as “swiming” in freeform text  106 , the presence of both words in accompaniment of other aquatic activities will result in both “swimming” and “swiming” being included in the words  134   a - 134   n  used in developing the user interest group model  142 . 
     The user interest group model  142  is passed from the model generation subsystem  112  to the user interest group vector generation module  150  in the run-time subsystem  114 . Using the user interest group model  142 , the user interest group vector generation module  150  determines a user interest group membership vector  152   a - 152   n  for each user  102   a - 102   n . Importantly, the user interest group membership vector  152  is determined for all users  102 , including those users  102   a - 102   n  whose freeform text  106   a - 106   n  was used by the evaluation system  110  as a basis for developing the user interest group model  142  as well as new users whose freeform text  106  has not been previously handled by the evaluation system  110 . The user interest group vector generation module  150  determines the user interest group membership vector  152   a  for a particular user  102   a  by ranking the words used in the freeform text  106   a  provided by the particular user  102   a  that are included in the vocabulary  134   a - 134   m  of the user interest group model  142 . Further, the user interest group vector generation module  150  identifies the user interest groups  144   a - 144   n  in which the words included in the freeform text  106   a  are most likely to occur. 
     The user interest group membership vector  152  for each user is a set of elements between zero (0) and one (1) indicative of the partial membership in each topic. In other words, a user  102  is considered to have a total interest level of 100%, the individual components or elements  154   a - 154   n  that form the user interest group membership vector  152  are indicative of relative allocation of the user&#39;s interest in the user interest group based on the word provided by the respective user in the freeform text  106  portion of the respective user&#39;s profile  104 . The user interest group membership vector  152  is therefore logically associable with a user  102  via the freeform text  106  provided by the respective user  102  which used to generate the respective user interest group membership vector  152 . 
     The user interest group vector generation module  150  performs post processing on the user interest group membership vector  152 . Usually the user interest group membership vector  152  for each user will include elements  154   a - 154   n  for each user interest group  144   a - 144   n , with some of the values being quite small. To isolate the dominant topics, the user interest group vector generation module  150  may set at least a portion of the elements  154   a - 154   n  equal to a value of zero (0). In one implementation, elements  154   a - 154   n  having an associated value of less than 1/(number of user interest groups  144   a - 144   n ) are set to a value of zero. 
     The user interest group vector generation module  150  also normalizes each user interest group membership vector  152 . In some implementations, the user interest group vector generation module  150  normalizes the element  154   a - 154   n  values by summing the element values and dividing each element value by the element value total (i.e., percentage normalization). In other implementations, the user interest group vector generation module  150  normalizes the user interest group membership vector  152  by converting the vector to unit length (i.e., unit normalization). 
     The user interest group vector generation module  150  then logically associates each of the user interest group membership vectors  152   a - 152   n  with a respective one of the users  102   a - 102   n  associated with the user profile  104   a - 104   n  containing the freeform text  106   a - 106   n  used to provide the respective user interest group membership vector  152   a - 152   n . In some implementations, the user interest group membership vector  152  and data indicative of the logically associated user  102  are stored in one or more data storage structures (e.g., databases) retained on one or more nontransitory storage media  156 . 
     Periodically, continuously, or from time-to-time, the service provider  108  may autonomously communicate and/or the user interest group vector generation module  150  may poll the service provider  108  to detect user profiles  104  associated with new users  102  or existing users who change their freeform text  106 . The user profiles  104   a - 104   n  associated with new users  102  or with existing users  102  who have changed and/or edited their freeform text  106  are received by the user interest group vector generation module  150 . Using the new and/or updated freeform text  106 , the user interest group vector generation module  150  generates a new or updated user interest group membership vector  152  for the respective new or existing user. 
     During run-time operation, the user interest matching module  160  compares a first user interest group membership vector  152   a  with any number of second user interest group membership vectors  152   b - 152   n  to identify candidate matching users having interests compatible with the interests provided by the first user  102   a . In some instances, the user interest matching module  160  identifies a candidate match by identifying a similarity between the user interest group membership vector  152   a  of the first user  102   a  and the user interest group membership vector  152   b  of a second user  102   b . In such instances, the user interest matching module  160  may determine a cosine similarity via the following: 
     
       
         
           
             
               
                 
                   
                     
                       s 
                       ⁡ 
                       
                         ( 
                         
                           
                             x 
                             → 
                           
                           , 
                           
                             y 
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                     = 
                     
                       
                         
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                           → 
                         
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                           → 
                         
                       
                       
                         
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                         ⁢ 
                         
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                             → 
                           
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                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       Where 
                       : 
                       
                         
                            
                           
                               
                           
                            
                         
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                         denotes 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         the 
                         ⁢ 
                         
                             
                         
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                         norm 
                       
                     
                     , 
                     
                       i 
                       . 
                       e 
                       . 
                     
                     , 
                     
                       
                          
                         
                           x 
                           → 
                         
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                       = 
                       
                         
                           
                             x 
                             → 
                           
                           · 
                           
                             x 
                             → 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     Equation (5) provides a value ranging from 0 to 1 that is indicative of the cosine similarity between the two user interest group membership vectors  152   a ,  152   b . Using equation (5), the greater the value, the greater the degree of similarity. Candidate matches may be identified as those users whose user interest group membership vector  152  provide a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges. Alternately, candidate matches may be identified as those users whose user interest group membership vector  152  provide a simple ranked list of values. 
     In other instances, the user interest matching module  160  identifies a candidate match by identifying a distance between the user interest group membership vector  152   a  of the first user  102   a  and the user interest group membership vector  152   b  of a second user  102   b . In such instances, the user interest matching module  160  may determine a Euclidean distance via the following: 
     
       
         
           
             
               
                 
                   
                     d 
                     ⁡ 
                     
                       ( 
                       
                         
                           x 
                           → 
                         
                         , 
                         
                           y 
                           → 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                        
                       
                         
                           x 
                           → 
                         
                         - 
                         
                           y 
                           → 
                         
                       
                        
                     
                     
                       
                          
                         
                           x 
                           → 
                         
                          
                       
                       + 
                       
                          
                         
                           y 
                           → 
                         
                          
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     Equation (6) provides a value indicative of the Euclidean distance between the two user interest group membership vectors  152   a ,  152   b . Using equation (6), the smaller the value, the lesser the distance and the greater the degree of similarity between the two user interest group membership vectors  152   a ,  152   b . Candidate matches may be identified as those users whose user interest group membership vector  152  provide a Euclidean distance value lesser than one or more defined threshold values or falling within one or more defined threshold ranges. 
     Other algorithms and methods may be employed by the user interest matching module  160  to determine the degree of similarity between two user interest group membership vectors  152  and consequently to identify candidate or potential matches. For example, in some implementations, one or more elements  154   a - 154   n  in the user interest group membership vector  152  may be weighted more heavily than others to increase the emphasis on the particular user interest group  144  associated with the weighted element  154  in identifying candidate or potential matches. In some implementations, one or more elements  154   a - 154   n  in the user interest group membership vector  152  may be discounted or weighted more lightly than others to decrease the emphasis on the particular user interest group  144  associated with the weighted element  154  in identifying candidate or potential matches. 
     The user interest matching module  160  ranks the candidate matches based on the determined value of similarity, distance, or other unit of measure between the user interest group membership vector  152   a  of a first user  102   a  and the user interest group membership vector  152   b - 152   n  of any number of other individual users or groups of users  102   b - 102   n.    
       FIG. 3  shows an evaluation system environment  300  in which the model generation subsystem  112  and the run-time subsystem  114  are provided by a single system that includes one or more processing units  304  (only one illustrated) and one or more associated nontransitory machine-readable storage media  156  (only one illustrated). The associated nontransitory computer- or processor-readable storage media  156  is communicatively coupled to the evaluation system  110  via one or more communications channels, for example one or more parallel cables, serial cables, or wireless network channels capable of high speed communications, for instance via Universal Serial Bus (“USB”) 3.0 or via Thunderbolt®. 
     Although the evaluation system  110  is described herein in the context of a standalone system, the evaluation system  110  may, in fact, constitute only a portion of a larger entity, for example a relationship building Website, Internet Service Provider (ISP) server, or similar entity. In such instances, components, sub-systems, and resources described forming all or a portion of the evaluation system  110  should be understood as components, subsystems, and resources that are shared with the relationship building Website or one or more common systems and/or resources that are allocated between the relationship building Website and the evaluation system  110 . 
     The evaluation system environment  300  may employ other computer systems and network equipment, for example additional servers, cloud computing services, proxy servers, firewalls, routers and/or bridges. The evaluation system  110  will at times be referred to in the singular herein, but this is not intended to limit the embodiments to a single device since in typical embodiments there may be more than one evaluation system  110  used. Unless described otherwise, the construction and operation of the various system and/or component blocks shown in  FIG. 3  are of conventional design. As a result, such system and/or component blocks need not be described in further detail herein, as they will be understood by those skilled in the relevant art(s). 
     The evaluation system  110  may include one or more processing units, logic circuits, and/or processors  304 , a system memory  314  and a system bus  316  that couples various system components including the system memory  314  to the processors  304 . The processors  304  may include any logic processing unit, such as one or more single or multi-core or processor microprocessors, central processing units (CPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), etc. The system bus  316  can employ any known bus structures or architectures, including a memory bus with memory controller, a peripheral bus, and/or a local bus. The system memory  314  includes read-only memory (“ROM”)  318  and random access memory (“RAM”)  320 . A basic input/output system (“BIOS”)  322 , which can form part of the ROM  318 , contains basic routines that help transfer information between elements within the evaluation system  300 , such as during start-up. 
     The evaluation system  110  may optionally include a hard disk drive  324  for reading from and writing to a hard disk  326 , an optical disk drive  328  for reading from and writing to removable optical disks  332 , and/or a magnetic disk drives  330  for reading from and writing to magnetic disks  334 . The optical disk  332  can be a CD-ROM, while the magnetic disk  334  can be a magnetic floppy disk or diskette. The hard disk drive  324 , optical disk drive  328  and magnetic disk drive  330  may communicate with the processors  304  via the system bus  316 . The hard disk drive  324 , optical disk drive  328  and magnetic disk drive  330  may include interfaces or controllers (not shown) coupled between such drives and the system bus  316 , as is known by those skilled in the relevant art. The drives  324 ,  328  and  330 , and their associated computer-readable media  326 ,  332 ,  334 , provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data used by the model generation subsystem  112  and the run-time subsystem  114  and/or the evaluation system  110 . Although the evaluation system  110  is illustrated employing a hard disk  324 , optical disk  328  and magnetic disk  330 , those skilled in the relevant arts will appreciate that other types of processor-readable media that can store data accessible by the processors  304  may be employed. All or a portion of such storage may be local to the evaluation system  110  (i.e., local storage) or remote from the evaluation system  110  (i.e., network storage or “cloud”/Internet storage) and may include any type of current or future developed nontransitory storage media including atomic/quantum/molecular storage, electro-resistive storage, WORM drives, RAID drives, removable and/or non-removable flash memory cards, digital video disks (“DVD”), Bernoulli cartridges, RAMs, ROMs, smart cards, etc. 
     Program modules are stored at least in part in the system memory  314 . Program modules may include processor-executable instruction sets, logic, and/or programming capable of causing the processors  304  to provide the evaluation system  110 . Program modules may include processor-executable instruction sets such as an operating system  336 , one or more application programs  338 , other programs or modules  340  and program data  342 . 
     The one or more application programs  338  may include one or more sets of logic or processor-readable instruction sets that cause the processor(s)  304  to provide or otherwise implement all or a portion of the preprocessing module  120 , the user interest term matrix generation module  130 , the user interest group generation module  140 , the user interest group vector generation module  150 , and the user interest matching module  160 . 
     One or more application programs  338  may implement all or a portion of the preprocessing module  120 . Such application programs  338  may remove non-letter characters and stopwords from the freeform text  106   a - 106   n  as described with regard to  FIG. 1 . In some implementations, such application programs  338  may stem all or a portion of the words included in the freeform text  106   a - 106   n , using a technique such as Porter stemming as described with regard to  FIG. 1 . In some implementations, such application programs  338  may determine the term frequency-inverse document frequency (tf-idf) for each of the words included in the freeform text  106   a - 106   n  of the user profiles  104   a - 104   n , using a technique such as described with regard to  FIG. 1 . 
     One or more application programs  338  may implement all or a portion of the user interest term matrix generation module  130 . Such application programs  338  may generate a user interest term matrix  132  that indicates the frequency of use of words  134   a - 134   m  in the freeform text  106   a - 106   n  portions of some or all of the user profiles  104   a - 104   n , using a technique such as described with regard to  FIG. 1 . 
     One or more application programs  338  may implement all or a portion of the user interest group generation module  140 . Such application programs may accept an input indicative of a defined number of user interest groups  144   a - 144   n , and then generate a user interest group model  142  in which a probability value for all of the words included in the user interest term matrix  132  are determined for each of the user interest groups  144   a - 144   n , using a technique such as described with regard to  FIG. 1 . 
     In some instances, the user interest group model  142  may be generated using a generative Natural Language Processing (NLP) model. In some instances, the NLP model may use a hierarchical Bayesian influence to discover latent similarities between the various freeform text entries  106   a - 106   n  of the users  102   a - 102   n . In at least one implementation the NLP model may include Latent Dirichlet Allocation (LDA). In some implementations, the application programs  338  may include one or more optimization routines to autonomously determine an optimal number of defined user interest groups  144   a - 144   n , for example using an internal or external trained machine learning system. 
     One or more application programs  338  may implement all or a portion of the user interest group vector generation module  150 . Such application programs may use the user interest group model  142  to generate user interest group membership vectors  152   a - 152   n , post-process the generated user interest group membership vectors  152   a - 152   n , and logically associate each of the user interest group membership vectors  152  with a respective one of the users  102   a - 102   n , using techniques such as those described with regard to  FIG. 1 . 
     One or more application programs  338  may implement all or a portion of the user interest matching module  160 , using techniques such as those described with regard to  FIG. 1 . Such user matching may include autonomously identifying potential or candidate matches including users  102  and/or groups of users  102   a - 102   n  based on a user interest group membership vector  152   a  associated with a particular user  102   a.    
     One or more application programs  338  may cause the model generation subsystem  112  of the evaluation system  110  to autonomously, periodically, intermittently, or from time-to-time, receive user profiles  104   a - 104   n  from the service provider  108 . Such enables the model generation subsystem  112  to autonomously update the user interest group model  142  provided by the user interest group generation module  140 . Updating the user interest group model  142  beneficially incorporates the evolution of user vernacular as evidenced by the freeform text  106   a - 106   n  provided by users  102   a - 102   n  into the evaluation system  110 . 
     One or more application programs  338  may cause the run-time subsystem  114  of the evaluation system  110  to autonomously, periodically, intermittently, or from time-to-time, receive user profiles  104   a - 104   n  from the service provider  108 . Such beneficially enables the run-time subsystem  114  to autonomously update the user interest group membership vectors  152   a - 152   n  for existing users  102   a - 102   n  as well as add new user interest group membership vectors  152  for new users  102 . 
     While shown in  FIG. 3  as being stored in the system memory  314 , the operating system  336 , application programs  338 , other programs/modules  340 , program data  342  and browser  344  can be stored on the hard disk  326  of the hard disk drive  324 , the optical disk  332  of the optical disk drive  328  and/or the magnetic disk  334  of the magnetic disk drive  330 . 
     An evaluation system operator and/or a service provider  108  can enter commands and information into the evaluation system  110  using one or more input devices such as a touch screen or keyboard  346  and/or a pointing device such as a mouse  348 , and/or via a graphical user interface. Other input devices can include a microphone, joystick, game pad, tablet, scanner, etc. These and other input devices are connected to the processor(s)  304  through an interface  350  such as a serial port interface that couples to the system bus  316 , although other interfaces such as a parallel port, a game port or a wireless interface or a universal serial bus (“USB”) can be used. A monitor  352  or other display device is coupled to the system bus  316  via a video interface  354 , such as a video adapter. The evaluation system  110  can include other output devices, such as speakers, printers, etc. 
       FIG. 4  shows a high-level method  400  of generating user interest group membership vectors  152  and using the generated user interest group membership vectors  152  to identify candidate or prospective user matches within a number of users  102   a - 102   n , according to one illustrated embodiment. Each user  102   a - 102   n  provides a respective user profile  104   a - 104   n  as part of a “sign-up” and/or registration process with a service provider  108 . Each of the user profiles  104   a - 104   n  includes a respective freeform text entry  106   a - 106   n . The words selected by the user  102  for inclusion in their freeform text  106  provide significant insight into the user&#39;s interests that transcends mere words. Considered together, the words selected by the user  102  can provide insight into larger concepts and interests useful for selecting candidate or prospective matches from a number of users  102   a - 102   n . The method  400  of analyzing the freeform text  106   a - 106   n  provided by the users  102   a - 102   n , generating user interest groups  144   a - 144   n , and generating user interest group membership vectors  152   a - 152   n  useful for identifying candidate or potential matches in a number of users  102   a - 102   n  commences at  402 . 
     At  404 , the model generation subsystem  112  derives a defined number of user interest groups  144   a - 144   n  from a plurality of user interests provided by a plurality of users  102   a - 102   n  in the form of freeform text  106   a - 106   n.    
     In some implementations, the model generation subsystem  112  forms words  134   a - 134   m  extracted from the freeform text  106   a - 106   n  into a user interest term matrix  132 . The user interest term matrix  132  is used by the user interest group generation module  140  to generate the user interest group model  142 . The user interest group model  142  matrix provides a probability value for each word  134   a - 134   m  in each of the defined number of user interest groups  144   a - 144   n . The probability value associated with a particular word  134   a  in a particular user interest group  144   a  is indicative of the relationship between the word  134   a  and the respective user interest group  144   a.    
     The user interest group generator module  140  employs a generative Natural Language Processing (NLP) model using a hierarchical Bayesian inference to discover latent similarities between passages of freeform text  106   a - 106   n  included in any number of user profiles  104   a - 104   n . In at least some instances, the generative NLP model includes Latent Dirichlet Allocation (“LDA”) although other generative NLP models may be substituted. The generative NLP model discovers relationships between passages of freeform text  106   a - 106   n  and clusters such relations into a defined number of user interest groups  144   a - 144   n . Advantageously, the relationships discovered by the NLP model are not defined but instead are organic to the freeform text  106   a - 106   n  provided by the users  102   a - 102   n.    
     The user interest group model  142  is determined based at least in part on the, the vocabulary (“V”) of words  134   a - 134   m , the set (“M”) of freeform text  106   a - 106   n , and a defined number (“K”) of user interest groups  144   a - 144   n . Using a LDA model as an illustrative example, let:
         α=vector parameter of the Dirichlet prior on the per-document user interest group distribution   β=vector parameter of the Dirichlet prior on the per-user interest group word distribution   θ i =user interest group distribution for document i   φ k =word distribution for topic k   z ij =user interest group for the j th  word in document i   w ij =specific j th  word in document i       

     The user interest group generator module  140  attempts to infer both the defined number of user interest groups  144   a - 144   n  (θ i ) and the word  134   a - 134   m  distribution with each of the defined number of user interest groups  144   a - 144   n  (φ k ). With reference to the plate notation graphical model depicted in  FIG. 2 , the total probability may be expressed as:
 
 p ( {right arrow over (w)},{right arrow over (z)} ,{right arrow over (θ)},{right arrow over (φ)},α,β)=Π i=1   K   p (φ i ,β)Π j=1   M   p (θ j ,α)Π t=1   N     j     p ( z   j,t |θ j ) p ( w   j,t   |φZ   j,t )  (7)
 
Since the occurrence of words in the freeform text  106   a - 106   n  is known, the word  134   a - 134   m  distribution within the freeform text  106   a - 106   n  may be determined and the user interest group  144   a - 144   n  inferred using the following equation which may be referred to as the posterior distribution:
 
     
       
         
           
             
               
                 
                   
                     p 
                     ⁡ 
                     
                       ( 
                       
                         
                           θ 
                           → 
                         
                         , 
                         
                           ϕ 
                           → 
                         
                         , 
                         
                           
                             z 
                             → 
                           
                           | 
                           
                             w 
                             → 
                           
                         
                         , 
                         α 
                         , 
                         β 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           θ 
                           , 
                           ϕ 
                           , 
                           z 
                           , 
                           
                             w 
                             | 
                             α 
                           
                           , 
                           β 
                         
                         ) 
                       
                     
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           
                             w 
                             | 
                             α 
                           
                           , 
                           β 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     The denominator of equation (4) is intractable to exact determination as it involves multiple integrals that are not separable or expressible in closed form. Consequently, approximation techniques such as Variational Expectation Maximization and Collapsed Gibbs Sampling may be used to approximate the value of the denominator in equation (8) and determine the probability value p({right arrow over (θ)}, {right arrow over (φ)}, {right arrow over (z)}|{right arrow over (w)}, α, β). The resultant value provides a probability that a particular word  134  is related to a particular user interest group  144 . Each word  134   a - 134   m  extracted from the freeform text  106   a - 106   n  provided by users  102   a - 102   n  is similarly evaluated for each user interest group. 
     The model generation subsystem  112  generates the user interest group model  142  using freeform text  106   a - 106   n  provided by users  102   a - 102   n . Advantageously, the model generation subsystem  112  may thus generate a user interest group model  142  using any defined language or any defined dialect of a language to reflect local usage or idioms. Additionally, since the model generation subsystem  112  uses both words  134   a - 134   n  and the relationship or logical grouping of words  134   a - 134   n  within the freeform text  106   a - 106   n  to determine user interest groups  144   a - 144   n , commonly misspelled words and alternative words are ranked appropriately within each of the interest groups  144  based on their appearance with other related concepts. Thus, for example, if the term “snowboarding” is commonly misspelled as “sno-boarding” the presence of either term in conjunction with other winter sports would result in a relatively high ranking for both terms in the user interest group  144  that is associated with winter sports. 
     At  406 , the user interest group vector generation module  150  receives the user interest term matrix  132  and determines for each user  102   a - 102   n  a respective user interest group membership vector  152   a - 152   n . The user interest group membership vector  152  includes “n” elements  154   a - 154   n , each element includes a value indicative of the strength of the user&#39;s interest in the respective user interest group  144   a - 144   n.    
     Since the user interest group vector generation module  150  generates the user interest group membership vectors  152   a - 152   n  using the user interest group model  142 , the user interest group vector generation module  150  can generate user interest group membership vectors  152   a - 152   n  using any defined language or any defined dialect of a language to reflect local usage or idioms. 
     At  408 , the user interest matching module  160  receives the user interest group membership vectors  152   a - 152   n  for any number of users  102   a - 102   n  and assesses a particular user  102  based on the user interest group membership vector  152  associated with the particular user  102 . The method  400  of analyzing the freeform text  106   a - 106   n  provided by the users  102   a - 102   n , generating user interest groups  144   a - 144   n , and generating user interest group membership vectors  152   a - 152   n  useful for identifying candidate or potential matches in a number of users  102   a - 102   n  concludes at  410 . 
       FIG. 5  shows a high-level method  500  of identifying candidate matches for a user  102   a  by comparing the logically associated user interest group membership vector  152   a  with user interest group membership vectors  152   b - 152   n  logically associated with other users  102   b - 102   n , according to one illustrated embodiment. The method of identifying potential or candidate matches for a user  102   a  by comparing the logically associated user interest group membership vector  152   a  with user interest group membership vectors  152   b - 152   n  logically associated with other users  102   b - 102   n  commences at  502 . 
     At  504 , the user interest matching module  160  compares the user interest group membership vector  152   a  logically associated with a first user  102   a  with at least one other user interest group membership vector  152   b - 152   n  logically associated with at least one other user  102   b - 102   n  to identify candidate matches for the first user  102   a.    
     In some implementations, the identification of candidate matches may include determining a value representative of a cosine similarity between the user interest group membership vector  152  provided by particular user  102  and the user interest group membership vector  152   a - 152   n  for each of the number of users  102   a - 102   n . In such implementations, the user interest matching module  160  may determine a cosine similarity via the following: 
     
       
         
           
             
               
                 
                   
                     
                       s 
                       ⁡ 
                       
                         ( 
                         
                           
                             x 
                             → 
                           
                           , 
                           
                             y 
                             → 
                           
                         
                         ) 
                       
                     
                     = 
                     
                       
                         
                           x 
                           → 
                         
                         · 
                         
                           y 
                           → 
                         
                       
                       
                         
                            
                           
                             x 
                             → 
                           
                            
                         
                         ⁢ 
                         
                            
                           
                             y 
                             → 
                           
                            
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     
                       Where 
                       : 
                       
                         
                            
                           
                               
                           
                            
                         
                         ⁢ 
                         denotes 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         the 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         norm 
                       
                     
                     , 
                     
                       i 
                       . 
                       e 
                       . 
                     
                     , 
                     
                       
                          
                         
                           x 
                           → 
                         
                          
                       
                       = 
                       
                         
                           
                             x 
                             → 
                           
                           · 
                           
                             x 
                             → 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     Equation (9) provides a value ranging from 0 to 1 that is indicative of the cosine similarity between two user interest group membership vectors  152   a ,  152   b . Using equation (9), the greater the value, the greater the degree of similarity. Candidate matches for the first user  102   a  may be identified by the run-time subsystem  114  as those users whose user interest group membership vector  152  provides a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges. Alternately, matches for the first user  102   a  may be identified by the run-time subsystem  114  as those users whose user interest group membership vector  152  provides a simple ranked list of values. 
     In some implementations, the identification of candidate matches may include determining a value representative of a Euclidean distance between the user interest group membership vector  152  provided by particular user  102  and the user interest group membership vector  152   a - 152   n  for each of the number of users  102   a - 102   n . In such implementations, the user interest matching module  160  may determine the Euclidean distance via the following: 
     
       
         
           
             
               
                 
                   
                     d 
                     ⁡ 
                     
                       ( 
                       
                         
                           x 
                           → 
                         
                         , 
                         
                           y 
                           → 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                        
                       
                         
                           x 
                           → 
                         
                         - 
                         
                           y 
                           → 
                         
                       
                        
                     
                     
                       
                          
                         
                           x 
                           → 
                         
                          
                       
                       + 
                       
                          
                         
                           y 
                           → 
                         
                          
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     Equation (10) provides a value indicative of the Euclidean distance between two user interest group membership vectors  152   a ,  152   b . Using equation (10), the smaller the value, the greater the degree of similarity between the two user interest group membership vectors  152   a ,  152   b . Candidate matches for the first user  102   a  may be identified by the run-time subsystem  114  as those users whose user interest group membership vector  152  provides a Euclidean distance value below one or more defined threshold values or falling within one or more defined threshold ranges. The method of identifying potential or candidate matches for a user  102   a  by comparing the logically associated user interest group membership vector  152   a  with user interest group membership vectors  152   b - 152   n  logically associated with other users  102   b - 102   n  concludes at  506 . 
       FIG. 6  shows a high-level method  600  of selecting a number of users  102   b - 102   n  from a larger user population to provide candidate matches for a first user  102   a  based on at least one non-interest related selection criteria, according to one illustrated embodiment. In at least some implementations, the service provider  108  or one or more front-end, non-interest based, matching systems (not shown) may select or otherwise choose a number of users  102   b - 102   n  from a larger user population. Such a selection process may, in some implementations, be used as a preliminary screening prior to evaluating the number of users  102   b - 102   n  via the evaluation system  110 . In some implementations, the service provider  108  or front-end, non-interest based, matching system may select each user included in the number of users  102   b - 102   n  based at least in part on one or more non-interest related criteria to provide a smaller pool of candidate matches for the first user  102   a . The method  600  of selecting a number of users  102   b - 102   n  from a larger user population to provide candidate matches for a first user  102   a  based on at least one non-interest related selection criteria commences at  602 . 
     At  604 , the service provider  108  or another front-end, non-interest based, matching system selects a number of users  102   a - 102   n  as candidate matches for a first user  102   a . In some implementations, such a selection process is performed upstream of the evaluation system  110  and relies upon criteria other than user interests. For example, if the first user  102   a  is male, the service provider  108  or front-end, non-interest based, matching system may select a number of users  102   b - 102   n  that are female. In another example, if the first user  102   a  is a female with a preference for males between the ages of 30 and 40 with incomes in excess of $100,000, the service provider  108  or front-end, non-interest based, matching system may select a number of users meeting the non-interest based preferences expressed by the first user  102   a.    
     The compatibility of the first user  102   a  with each of the users included in the number of users  102   b - 102   n  may be assessed by the evaluation system  110  using the user interest group membership vectors  152   b - 152   n  associated with the number of users  102   b - 102   n  selected from the larger user population. The method  600  of selecting a number of users  102   b - 102   n  from a larger user population to provide candidate matches for a first user  102   a  based on at least one non-interest related selection criteria commences at  606 . 
       FIG. 7  shows a high-level method  700  of normalizing the user interest group membership vectors  152   a - 152   n  and assessing the normalized user interest group membership vectors  152   a - 152   n  to identify candidate or potential matches, according to one illustrated embodiment. The method  600  of normalizing the user interest group membership vectors  152   a - 152   n  and using the normalized user interest group membership vectors  152   a - 152   n  to identify candidate matches commences at  702 . 
     At  704 , the user interest group vector generation module  150  normalizes the user interest group membership vectors  152   a - 152   n . In some implementations, the user interest group vector generation module  150  normalizes the element  154   a - 154   n  values in each user interest group membership vector  152  by summing the element values and dividing each respective element value by the element value total (i.e., percentage normalization). In other implementations, the user interest group vector generation module  150  normalizes the user interest group membership vector  152  by converting the vector to unit length (i.e., unit normalization). 
     At  706 , the user interest matching module  160  assess some or all of the user interest group membership vectors  152   a - 152   n  normalized at  604  to identify candidate matches. The method  700  of normalizing the user interest group membership vectors  152   a - 152   n  and using the normalized user interest group membership vectors  152   a - 152   n  to identify candidate matches concludes at  708 . 
       FIG. 8  shows a high-level method  800  of receiving a plurality of user interests as freeform text  106   a - 106   n  and preprocessing the freeform text  106   a - 106  using any number of preprocessing techniques. At times, such preprocessing of the freeform text  106   a - 106   n  is performed prior to introducing the freeform text  106  to the user interest term matrix generation module  130 . The text entered by users  102   a - 102   n  as freeform text  106   a - 106   n  may include different words having similar meaning. For example, a first user may simply enter his/her interest as “skiing” while a second user may enter his/her interest as “I like to ski.” Although both users have entered a similar interest, different words have been used. Additionally, freeform text may contain non-letter characters and word such as articles (a, the, etc.) and personal pronouns (I, me, my, etc.) that provide no additional information relevant to ascertaining a user&#39;s interests by the evaluation system  110 . In such instances, the preprocessing the freeform text  106   a - 106   n  prior to introducing the text to the user interest term matrix generation module  130  may beneficially improve the quality of both the user interest group model  142 , the user interest group membership vectors  152   a - 152   n  generated using the user interest group model  142 , and the candidate matches identified using the user interest group membership vectors  152   a - 152   n . The method  700  of preprocessing the freeform text  106   a - 106   n  commences at  802 . 
     At  804 , the preprocessing module  120  receives the freeform text  106   a - 106   n  from the service provider  108 . 
     At  806 , the preprocessing module  120  alters and/or edits the freeform text  106   a - 106   n  provided by the users  102   a - 102   n  preparatory to generating the user interest term matrix  132 . In some instances, such alteration and/or editing of the freeform text  106  improves the overall performance of the user interest group model generator  140 . The preprocessing module  120  removes non-letter characters from the freeform text  106 . The preprocessing module  120  also removes “stopwords” from the freeform text  106 . Stopwords include words such as articles (a, the, etc.), personal pronouns (I, me, my, etc.), and others that provide no indication of a user interest and are included in a defined dictionary accessible to the preprocessing module  120 . 
     In some implementations, the preprocessing module  120  may also optionally stem the words in the freeform text  106 . Such stemming may be performed using a Porter stemmer. A word stemmer stems different variations of a word back to a single word stem, for example “movie” and “movies” would both be stemmed to “movi.” A word stemmer thus serves to reduce the number of words included in a vocabulary without reducing the overall richness or variety of concepts conveyed by the words included in the vocabulary. The method  800  of preprocessing the freeform text  106   a - 106   n  concludes at  808 . 
       FIG. 9  shows a high-level method  900  of identifying candidate matches for a first user  102   a  based on the user interest group membership vector  152  generated using first user  102   a  provided freeform text  106   a , according to one illustrated embodiment. As part of the “sign-up” or registration process to participate in or receive services from the service provider  108 , each user  102  generates a user profile  104  that includes freeform text  106 . Considered individually, the words included in the freeform text  106  provided by the user  102  identify the interests of the respective user. Additionally, considered collectively, the words included in the freeform text  106  provided by the user  102  may identify broader interests or conceptual interests of the respective user  102 . The evaluation system  110  generates a user interest group membership vector  152  for the respective user  102  that considers broader concepts in the freeform text provided by the user  102 . The user interest matching module  160  uses the broader concepts when comparing user interest group membership vectors  152  to identify candidate matches. The method  800  of identifying candidate matches based on user supplied freeform text  106  commences at  902 . 
     At  904 , the user interest group vector generation module  150  receives the freeform text  106  from the user profile  104  associated with a first user  102   a . Responsive to the receipt of the freeform text  106 , the user interest group vector generation module  150  generates a user interest group membership vector  152   a  for the first user  102   a  based at least in part on the freeform text  106  provided by the first user  102   a.    
     At  906 , the user interest matching module  160  identifies candidate matches for the first user  102   a  using the logically associated user interest group membership vector  152   a . During run-time operation, the user interest matching module  160  compares the first user interest group membership vector  152   a  with any number of second user interest group membership vectors  152   b - 152   n  to identify candidate matching users having interests compatible with the interests provided by the first user  102   a.    
     In some instances, the user interest matching module  160  identifies a candidate match by identifying a similarity, such as the cosine similarity discussed above with regard to  FIG. 1 , between the user interest group membership vector  152   a  of the first user  102   a  and the user interest group membership vector  152   b  of a second user  102   b . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   a ,  152   b . The greater the cosine similarity value between two user interest group membership vectors  152   a ,  152   b , the greater the degree of similarity between the two vectors. Candidate matches may be identified as those users whose user interest group membership vector  152  provide a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges. Alternately, candidate matches may be identified as those users whose user interest group membership vector  152  provide a simple ranked list of values. 
     In other instances, the user interest matching module  160  identifies a candidate match by identifying a distance, for example a Euclidean distance discussed above with regard to  FIG. 1 , between the user interest group membership vector  152   a  of the first user  102   a  and the user interest group membership vector  152   b  of a second user  102   b . The user interest matching module  160  generates a value indicative of the Euclidean distance between two user interest group membership vectors  152   a ,  152   b . The smaller the Euclidean distance value, the lesser the distance and the greater the degree of similarity between the two user interest group membership vectors  152   a ,  152   b . Candidate matches may be identified as those users whose user interest group membership vector  152  provide a Euclidean distance value lesser than one or more defined threshold values or falling within one or more defined threshold ranges. The method  900  of identifying candidate matches based on user supplied freeform text  106  concludes at  908 . 
       FIG. 10  shows a high-level method  1000  of identifying candidate matches for a first user  102   a  based on a hypothetical user interest group membership vector  152  generated using first user  102   a  provided interests desired in a candidate match, according to one illustrated embodiment. At times, the first user  102   a  may have a known or defined set of interests they would find desirable in a candidate match. Such candidate matches may be identified by the evaluation system  110  in addition to or instead of candidate matches identified using the respective user&#39;s own interests provided as freeform text  106  in the user&#39;s profile  104 . The method  1000  of identifying candidate matches based on a hypothetical user interest group membership vector  152  generated via user supplied desired candidate match interests commences at  1002 . 
     At  1004 , the user interest group vector generation module  150  receives desired candidate match interests from a first user  102   a . Such desired candidate match interests may be provided by the first user  102   a  in the form of a freeform text entry  106  received by the service provider  108 . Responsive to the receipt of the freeform text  106  indicative of the desired candidate match interests, the user interest group vector generation module  150  generates a hypothetical user interest group membership vector  152  using the desired candidate match interests provided by the first user  102   a.    
     At  1006 , in addition to or instead of using the first user&#39;s user interest group membership vector  152   a , the user interest matching module  160  identifies candidate matches for the first user  102   a  using the hypothetical user interest group membership vector  152  generated at  804 . During run-time operation, the user interest matching module  160  compares the hypothetical user interest group membership vector  152  with any number of user interest group membership vectors  152   b - 152   n  to identify candidate matches having interests compatible with the desired interests provided by the first user  102   a.    
     In some instances, the user interest matching module  160  identifies a candidate match by identifying a similarity, such as the cosine similarity discussed above with regard to  FIG. 1 , between the hypothetical user interest group membership vector  152  and the user interest group membership vector  152   b  of a second user  102   b . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152 ,  152   b . The greater the cosine similarity value between two user interest group membership vectors  152 ,  152   b , the greater the degree of similarity between the two vectors. Candidate matches may be identified as those users whose user interest group membership vector  152  provide a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges. Alternately, candidate matches may be identified as those users whose user interest group membership vector  152  provide a simple ranked list of values. 
     In other instances, the user interest matching module  160  identifies a candidate match by identifying a distance, for example a Euclidean distance discussed above with regard to  FIG. 1 , between the hypothetical user interest group membership vector  152  and the user interest group membership vector  152   b  of a second user  102   b . The user interest matching module  160  generates a value indicative of the Euclidean distance between two user interest group membership vectors  152 ,  152   b . The smaller the Euclidean distance value, the lesser the distance and the greater the degree of similarity between the two user interest group membership vectors  152 ,  152   b . Candidate matches may be identified as those users whose user interest group membership vector  152  provide a Euclidean distance value lesser than one or more defined threshold values or falling within one or more defined threshold ranges. The method  1000  of identifying candidate matches based on a hypothetical user interest group membership vector  152  generated via user supplied desired candidate match interests concludes at  1008 . 
       FIG. 11  shows a high-level method  1100  of identifying candidate matches for a first user  102   a  based on a user interest group membership vector  152   b  logically associated with a second user  102   b  selected by the first user  102   a , according to one illustrated embodiment. At times, the first user  102   a  may be familiar with a second user  102   b  (e.g., the second user  102   b  may be a friend or acquaintance of the first user  102   a ) with whom they share mutually compatible interests that the first user  102   a  would find desirable in a candidate match. The user interest matching module  160  may identify such candidate matches in addition to or instead of candidate matches identified using the respective user&#39;s own logically associated user interest group membership vector  152   a . The method  1100  of identifying candidate matches based on a user interest group membership vector  152   b  logically associated with a second user  102   b  commences at  1102 . 
     At  1104 , the evaluation system  110  receives from a first user  102   a  data indicative of a second user  102   b  who shares mutually compatible interests that the first user  102   a  would find desirable in a candidate match. 
     At  1106 , in addition to or instead of using the first user&#39;s user interest group membership vector  152   a , the user interest matching module  160  identifies candidate matches for the first user  102   a  using the user interest group membership vector  152  logically associated with the second user  102   b . During run-time operation, the user interest matching module  160  compares the second user&#39;s user interest group membership vector  152   b  with any number of user interest group membership vectors  152   c - 152   n  to identify candidate matches having interests compatible with the second user  102   b . In some implementations, the user interest matching module  160  may specifically exclude candidate matches users having at least one non-interest related trait that disqualifies the candidate match (e.g., candidate matches that have compatible interests based the user interest group membership vectors  152 , but who are friends or family of the first users  102   a ). 
     In some instances, the user interest matching module  160  identifies a candidate match by identifying a similarity, such as the cosine similarity discussed above with regard to  FIG. 1 , between the second user&#39;s user interest group membership vector  152   b  and the user interest group membership vector  152   c  of another user  102   c . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   b ,  152   c . The greater the cosine similarity value between two user interest group membership vectors  152   b ,  152   c , the greater the degree of similarity between the two vectors. Candidate matches may be identified as those users whose user interest group membership vector  152  provide a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges of the second user&#39;s user interest group membership vector  152   b . Alternately, candidate matches may be identified as those users whose user interest group membership vector  152  provide a simple ranked list of values. 
     In other instances, the user interest matching module  160  identifies a candidate match by identifying a distance, for example a Euclidean distance discussed above with regard to  FIG. 1 , between the second user&#39;s user interest group membership vector  152   b  and the user interest group membership vector  152   c  of another user  102   c . The user interest matching module  160  generates a value indicative of the Euclidean distance between two user interest group membership vectors  152   b ,  152   c . The smaller the Euclidean distance value, the lesser the distance and the greater the degree of similarity between the two user interest group membership vectors  152   b ,  152   c . Candidate matches may be identified as those users whose user interest group membership vector  152  provide a Euclidean distance value lesser than one or more defined threshold values or falling within one or more defined threshold ranges of the second user&#39;s user interest group membership vector  152   b . The method  1100  of identifying candidate matches based on a user interest group membership vector  152   b  logically associated with a second user  102   b  concludes at  1108 . 
       FIG. 12  shows a high-level method  1100  of identifying candidate matches for a first user  102   a  based on a user interest group membership vector  152   b  logically associated with a second user  102   b  selected based on one or more actions taken and/or activities performed the first user  102   a , according to one illustrated embodiment. At times, the actions or activities of a first user  102   a  may provide insight into compatible matches for the first user  102   a . For example, the first user may hover a pointer over an image or a profile logically associated with a second user  102   b . Such interest in the second user  102   b , even though explicitly unexpressed by the first user  102   a  may be indicative that the first user  102   a  finds attractive and/or desirable and therefore indicates a suitable candidate match for the first user  102   a.    
     Based at least in part on the first user&#39;s actions and/or activities but not based on an express interest in the second user  102   b  by the first user  102   a , the service provider  108  may communicate data indicative of the second user  102   b  to the user interest matching module  160 . Such communication of data indicative of the second user  102   b  may be performed by the service provider  108  with or without the knowledge and/or consent of the first user  102   b . The user interest matching module  160  may use the user interest group membership vector  152   b  logically associated with the second user  102   b  to identify additional candidate matches in addition to or instead of candidate matches identified using the first user&#39;s own logically associated user interest group membership vector  152   a . The method  1200  of identifying candidate matches based on a user interest group membership vector  152   b  logically associated with a second user  102   b  selected based on one or more actions taken and/or activities performed the first user  102   a  commences at  1202 . 
     At  1204 , the service provider  108  detects a first user&#39;s interest in a second user  102   b  based on one or more actions or activities performed by the first user  102   a  and in the absence of an express indication by the first user  102   a  of an interest in the second user  102   b . Such actions or activities may include for example, the first user  102   a  hovering over an image and/or profile associated with the second user  102   b.    
     At  1206 , in addition to or instead of using the first user&#39;s user interest group membership vector  152   a , the user interest matching module  160  identifies candidate matches for the first user  102   a  using the user interest group membership vector  152   b  logically associated with the second user  102   b . During run-time operation, the user interest matching module  160  compares the second user&#39;s user interest group membership vector  152   b  with any number of user interest group membership vectors  152   c - 152   n  to identify candidate matches having interests compatible with the second user  102   b.    
     In some instances, the user interest matching module  160  identifies a candidate match by identifying a similarity, such as the cosine similarity discussed above with regard to  FIG. 1 , between the second user&#39;s user interest group membership vector  152   b  and the user interest group membership vector  152   c  of another user  102   c . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   b ,  152   c . The greater the cosine similarity value between two user interest group membership vectors  152   b ,  152   c , the greater the degree of similarity between the two vectors. Candidate matches may be identified as those users whose user interest group membership vector  152  provide a cosine similarity value greater than one or more defined threshold values or falling within one or more defined threshold ranges of the second user&#39;s user interest group membership vector  152   b . Alternately, candidate matches may be identified as those users whose user interest group membership vector  152  provide a simple ranked list of values. 
     In other instances, the user interest matching module  160  identifies a candidate match by identifying a distance, for example a Euclidean distance discussed above with regard to  FIG. 1 , between the second user&#39;s user interest group membership vector  152   b  and the user interest group membership vector  152   c  of another user  102   c . The user interest matching module  160  generates a value indicative of the Euclidean distance between two user interest group membership vectors  152   b ,  152   c . The smaller the Euclidean distance value, the lesser the distance and the greater the degree of similarity between the two user interest group membership vectors  152   b ,  152   c . Candidate matches may be identified as those users whose user interest group membership vector  152  provide a Euclidean distance value lesser than one or more defined threshold values or falling within one or more defined threshold ranges of the second user&#39;s user interest group membership vector  152   b . The method  1200  of identifying candidate matches based on a user interest group membership vector  152   b  logically associated with a second user  102   b  selected based on one or more actions taken and/or activities performed the first user  102   a  commences at  1208 . 
       FIG. 13  shows a high-level method  1300  of assessing the compatibility of a first user  102   a  with at least one other user  102   b  included in a subset of users  102   b - 102   n  using the user interest group membership vectors  152   a  and  152   b , according to one illustrated embodiment. At times, it may be desirable to assess the compatibility of a first user  102   a  with a subset or group including a plurality of users  102   b - 102   n . The method  1300  of assessing the compatibility of a first user  102   a  with at least one second user  102   b  that is included in a subset or group including a plurality of users  102   b - 102   n  commences at  1302 . 
     At  1304 , a subset or group that includes a plurality of users  102   b - 102   n  is formed based on at least one user trait other than a user interest. Such traits may include, for example, a user&#39;s geographic location, a user&#39;s age, a user&#39;s sex, a user&#39;s orientation, a user&#39;s ethnicity, or any other non-interest related trait. In some instances, the service provider  108  may autonomously select some or all of the users  102   b - 102   n  included in the subset or group. In some instances, the evaluation system  110  may autonomously select some or all of the users included in the subset or group. 
     At  1306 , the user interest matching module  160  assesses the first user  102   a  against at least one second user  102   b  included in the subset of users  102   b - 102   n  by comparing the first user&#39;s user interest group membership vector  152   a  with the user interest group membership vector  152   b  of the at least one second user  102   b  included in the subset or group. 
     In some instances, the user interest matching module  160  assesses the compatibility of a first user  102   a  with a second user  102   b  included in the subset or group of users  102   b - 102   n  by determining a value indicative of a similarity, such as a cosine similarity, between the first user&#39;s user interest group membership vector  152   a  and the second user&#39;s user interest group membership vector  152   b . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   a ,  152   b . The greater the cosine similarity value between two user interest group membership vectors  152   a ,  152   b , the greater the degree of similarity between the two vectors. 
     In other instances, the user interest matching module  160  assesses the compatibility of a first user  102   a  with a second user  102   b  included in the subset or group of users  102   b - 102   n  by determining a value indicative of a distance, such as the Euclidean distance, between the first user&#39;s user interest group membership vector  152   a  and the second user&#39;s user interest group membership vector  152   b . The smaller the Euclidean distance value between the user interest group membership vectors  152   a ,  152   b , the lesser the distance and the greater the degree of similarity between the two vectors. The method  1300  of assessing the compatibility of a first user  102   a  with at least one second user  102   b  that is included in a subset or group including a plurality of users  102   b - 102   n  concludes at  1308 . 
       FIG. 14  shows a high-level method  1400  of assessing the compatibility of a first user  102   a  with a plurality of other users  102   b - 102   n  by comparing the first user&#39;s user interest group membership vector  152   a  with a hypothetical user interest group membership vector  152  generated using the user interest group membership vectors  152   b - 152   n  logically associated with the plurality of users  102   b - 102   n , according to one illustrated embodiment. The method  1400  of assessing the compatibility of a first user  102   a  with a plurality of other users  102   b - 102   n  commences at  1402 . 
     At  1404 , the evaluation system  110  generates a hypothetical user interest group membership vector  152  using the individual user interest group membership vectors  152   b - 152   n  of the users  102   b - 102   n  included in the plurality of users  102   b - 102   n . In some instances, the plurality of users  102   b - 102   n  may include users having at least one demographic attribute in common. Example demographic attributes include, but are not limited to, a user&#39;s gender, a user&#39;s age, a user&#39;s income, a user&#39;s orientation, a user&#39;s religion, and a user&#39;s ethnicity. In some instances, the plurality of users  102   b - 102   n  may include users sharing a defined geographic locale. Example geographic locales include, but are not limited to, a user&#39;s city, a user&#39;s state, and a user&#39;s nation. In some instances, the hypothetical user interest group membership vector  152  may be determined using at least one of a mean or a median of the user interest group membership vectors  152   b - 152   n  of the users  102   b - 102   n  included in the plurality of users. 
     At  1406 , the user interest matching module  160  assesses the compatibility of the first user  102   a  with the other users  102   b - 102   n  included in the plurality of users by comparing the first user&#39;s user interest group membership vector  152   a  with the hypothetical user interest group membership vector  152  generated at  1404 . 
     In some instances, the user interest matching module  160  assesses the compatibility of the first user  102   a  with the other users  102   b - 102   n  included in the plurality of users by determining a value indicative of a similarity, such as a cosine similarity, between the first user&#39;s user interest group membership vector  152   a  and the hypothetical user interest group membership vector  152  generated at  1404 . The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   a ,  152 . The greater the cosine similarity value between two user interest group membership vectors  152   a ,  152 , the greater the degree of similarity between the two vectors. 
     In other instances, the user interest matching module  160  assesses the compatibility of the first user  102   a  with the other users  102   b - 102   n  included in the plurality of users by determining a value indicative of a distance, such as the Euclidean distance, between the first user&#39;s user interest group membership vector  152   a  and the hypothetical user interest group membership vector generated at  1404 . The smaller the Euclidean distance value between the user interest group membership vectors  152   a ,  152 , the lesser the distance and the greater the degree of similarity between the two vectors. 
     In some implementations, the evaluation system  110  may provide an output to the service provider that includes a value or other data indicative of the degree of similarity and/or the degree of difference (e.g., the distance) between the user interest group membership vectors  152   a ,  152 . At times, the service provider  108  may display the value or other data indicative of the degree of similarity and/or the degree of difference between the user interest group membership vectors  152   a ,  152  to the first user  102   a  and/or some or all of the plurality of users  102   b - 102   n . The method  1400  of assessing the compatibility of a first user  102   a  with a plurality of other users  102   b - 102   n  concludes at  1408 . 
       FIG. 15  shows a high-level method  1500  that may be used in along with method  1300  (to determine the compatibility of the first user  102   a  with a first plurality of users  102   b - 102   n ) to identify at least one second plurality of users  102   b ′- 102   n ′ with whom the first user  102   a  may have a greater degree of compatibility, according to one illustrated embodiment. As discussed with regard to  FIG. 14 , the compatibility of the first user  102   a  with a first plurality of users  102   b - 102   n  may be assessed by generating a hypothetical user interest group membership vector  152  representative of the first plurality of users  102   b - 102   n  and determining the correlation, degree of similarity, or distance between the hypothetical user interest group membership vector  152  and the first user&#39;s user interest group membership vector  152   a . In some instances, the evaluation system  110  and/or the service provider  108  may autonomously identify a second plurality of users  102   b ′- 102   n ′ with whom the first user  102   a  has an even greater compatibility. The method  1500  of identifying a second plurality of users  102   b ′- 102   n ′ with whom the first user has a greater compatibility commences at  1502 . 
     At  1504 , at least one second plurality of users  102   b ′- 102   n ′ is created and a hypothetical user interest group membership vector  152 ′ is determined by the evaluation system  110  based at least in part on the individual user interest group membership vectors  152   b ′- 152   n ′. The user interest matching module  160  determines a respective hypothetical user interest group membership vector  152 ′ for the second plurality of users  102   b ′- 102   n ′. The user interest matching module  160  also determines the compatibility of the first user  102   a  with the second plurality of users  102   b ′- 102   n ′ by determining the correlation, degree of similarity, or distance between the hypothetical user interest group membership vector  152 ′ and the first user&#39;s user interest group membership vector  152   a.    
     By comparing the correlation, degree of similarity, or distance of between the first user&#39;s user interest group membership vector  152   a  and the user interest group membership vector  152  of the first plurality of users  102   b - 102   n  and the user interest group membership vector  152 ′ of the second plurality of users  102   b ′- 102   n ′, the user interest matching module  160  can determine whether a greater compatibility exists between the first user  102   a  and the first plurality of users  102   b - 102   n  or the second plurality of users  102   b ′- 102   n′.    
     In some instances, the users  102   b ′- 102   n ′ included in the second plurality of users may have at least one attribute or characteristic common to the users  102   b - 102   n  included in the first plurality of users and may have at least one attribute or characteristic that is not common to the users  102   b - 102   n  included in the first plurality of users. In some instances, the first plurality of users  102   b - 102   n  may be selected based upon sharing a first geographic locale and the second plurality of users  102   b ′- 102   n ′ may be selected based upon sharing a second geographic locale that is different than the first geographic locale. 
     At  1506 , the service provider  108  can provide an indication of the second plurality of users  102   b ′- 102   n ′ to the first user  102   a . The method  1500  of identifying a second plurality of users  102   b ′- 102   n ′ with whom the first user  102   a  has a greater compatibility than a first plurality of users  102   b - 102   n  concludes at  1508 . 
       FIG. 16  shows a high-level method  1600  of assessing the dissimilarity between a first user  102   a  and at least one other user  102   b  included in a subset of users  102   b - 102   n  using the user interest group membership vectors  152   a  and  152   b  logically associated with each of the users, according to one illustrated embodiment. At times, it may be desirable to assess the dissimilarity or to quantify the degree of dissimilarity existent between a first user  102   a  and a second user  102   b  included in a subset or group including a plurality of users  102   b - 102   n . The method  1600  of assessing the dissimilarity or degree of dissimilarity existent between a first user  102   a  and at least one second user  102   b  that is included in a subset or group including a plurality of users  102   b - 102   n  commences at  1602 . 
     At  1604 , a subset or group that includes a plurality of users  102   b - 102   n  is formed based on at least one user trait other than a user interest. Such traits may include, for example, a user&#39;s geographic location, a user&#39;s age, a user&#39;s sex, a user&#39;s orientation, a user&#39;s ethnicity, or any other non-interest related trait. In some instances, the service provider  108  may autonomously select some or all of the users  102   b - 102   n  included in the subset or group. In some instances, the evaluation system  110  may autonomously select some or all of the users included in the subset or group. 
     At  1606 , the user interest matching module  160  assesses the first user  102   a  against at least one second user  102   b  included in the subset of users  102   b - 102   n  by comparing the first user&#39;s user interest group membership vector  152   a  with the user interest group membership vector  152   b  of the at least one second user  102   b  included in the subset or group. 
     In some instances, the user interest matching module  160  assesses the dissimilarity or degree of dissimilarity existent between a first user  102   a  and at least one second user  102   b  included in the subset or group of users  102   b - 102   n  by determining a value indicative of a similarity, such as a cosine similarity, between the first user&#39;s user interest group membership vector  152   a  and the second user&#39;s user interest group membership vector  152   b . In such instances, the user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152   a ,  152   b . The lesser the cosine similarity value between two user interest group membership vectors  152   a ,  152   b , the lesser the degree of similarity between the two vectors and consequently, the greater the degree of dissimilarity in user interests between the first user  102   a  and the at least one second user  102   b.    
     In other instances, the user interest matching module  160  assesses the dissimilarity or degree of dissimilarity existent between a first user  102   a  and at least one second user  102   b  included in the subset or group of users  102   b - 102   n  by determining a value indicative of a distance, such as the Euclidean distance, between the first user&#39;s user interest group membership vector  152   a  and the at least one second user&#39;s user interest group membership vector  152   b . The greater the Euclidean distance value between the user interest group membership vectors  152   a ,  152   b , the greater the distance between the vectors and the lesser the degree of similarity between the two vectors and consequently, the greater the degree of dissimilarity in user interests between the first user  102   a  and the at least one second user  102   b . The method  1600  of assessing the dissimilarity or degree of dissimilarity existent between a first user  102   a  and at least one second user  102   b  that is included in a subset or group including a plurality of users  102   b - 102   n  concludes at  1608 . 
       FIG. 17  shows a high-level method  1700  of assessing the compatibility or similarity between a first plurality of the users and a second plurality of the users based on respective hypothetical interest group membership vectors for each of the first plurality of the users and the second plurality of the users, according to one illustrated embodiment. A first hypothetical interest group membership vector  152 ′ is determined at least in part based on the respective user interest group membership vectors  152   a ′- 152   n ′ associated with the users  102   a ′- 102   n ′ included in the first plurality of users. A second hypothetical interest group membership vector  152 ″ is determined at least in part based on the respective user interest group membership vectors  152   a ″- 152   n ″ associated with the users  102   a ″- 102   n ″ included in the second plurality of users. The first and the second hypothetical interest group membership vectors  152 ′ and  152 ″ are compared and the level of similarity or dissimilarity assessed. The method  1700  of assessing the compatibility or similarity between a first plurality of the users  102   a ′- 102   n ′ and a second plurality of the users  102   a ″- 102   n ″ based on respective hypothetical interest group membership vectors for each of the first plurality of the users and the second plurality of the users commences at  1702 . 
     At  1704 , the evaluation system  110  generates a first hypothetical user interest group membership vector  152 ′ using at least some of the individual user interest group membership vectors  152   a ′- 152   n ′ of the users  102   a ′- 102   n ′ included in the first plurality of the users. In some instances, some or all of the users  102   a ′- 102   n ′ selected for inclusion in the first plurality of the users may be autonomously determined, for example by the service provider  108 . In some instances, some or all of the users  102   a ′- 102   n ′ selected for inclusion in the first plurality of the users may be manually selected, for example based on the receipt by the service provider  108  of a user&#39;s interest in being included in the first plurality of the users  102   a ′- 102   n ′. The hypothetical user interest group membership vector  152 ′ logically associated with the first plurality of the users  102   a ′- 102   n ′ may be determined using at least one of a mean or a median of the user interest group membership vectors  152   a ′- 152   n ′ of the users  102   a ′- 102   n ′ included in the first plurality of the users. 
     At  1706 , the evaluation system  110  generates a second hypothetical user interest group membership vector  152 ″ using at least some of the individual user interest group membership vectors  152   a ″- 152   n ″ of the users  102   a ″- 102   n ″ included in the second plurality of the users. In some instances, some or all of the users  102   a ″- 102   n ″ selected for inclusion in the second plurality of the users may be autonomously determined, for example by the service provider  108 . In some instances, some or all of the users  102   a ″- 102   n ″ selected for inclusion in the second plurality of the users may be manually selected, for example based on the receipt by the service provider  108  of a user&#39;s interest in being included in the first plurality of the users  102   a ″- 102   n ″. The hypothetical user interest group membership vector  152 ″ logically associated with the second plurality of the users  102   a ″- 102   n ″ may be determined using at least one of a mean or a median of the user interest group membership vectors  152   a ″- 152   n ″ of the users  102   a ″- 102   n ″ included in the second plurality of the users. 
     At  1708 , the user interest matching module  160  assesses the first compatibility of the first hypothetical user interest group membership vector  152 ′ with the second user interest group membership vector  152 ″. In some instances, by determining the similarity between the two hypothetical user interest group membership vectors, the user interest matching module  160  is able to quantify a degree of similarity between the first plurality of the users  102   a ′- 102   n ′ and the second plurality of the users  102   a ″- 102   n ″. Such information may be useful, for example in assisting the service provider  108  in providing recommendations regarding whether a first group represented by the first plurality of the users  102   a ′- 102   n ′ would be compatible with a second group represented by the second plurality of users  102   a ″- 102   n″.    
     In some instances, the user interest matching module  160  assesses the compatibility of the first plurality of users  102   a   102   n ′ with the second plurality of users  102   a ″- 102   n ″ by determining a value indicative of a similarity, such as a cosine similarity, between the first hypothetical user interest group membership vector  152 ′ and the second hypothetical user interest group membership vector  152 ″. The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152 ′,  152 ″. 
     The greater the cosine similarity value between two hypothetical user interest group membership vectors  152 ′,  152 ″, the greater the degree of similarity between the two vectors. 
     In other instances, the user interest matching module  160  assesses the compatibility of the first plurality of users  102   a ′- 102   n ′ with the second plurality of users  102   a ″- 102   n ″ by determining a value indicative of a distance, such as the Euclidean distance, between the first hypothetical user interest group membership vector  152 ′ and the second hypothetical user interest group membership vector  152 ″. The smaller the Euclidean distance value between the hypothetical user interest group membership vectors  152 ′,  152 ″, the lesser the distance and the greater the degree of similarity between the two vectors. The method  1700  of assessing the compatibility or similarity between a first plurality of the users  102   a ′- 102   n ′ and a second plurality of the users  102   a ″- 102   n ″ based on hypothetical interest group membership vectors for each of the first plurality of the users and the second plurality of the users concludes at  1710 . 
       FIG. 18  shows a high-level method  1800  of determining a hypothetical user interest group membership vector  152  for a plurality of users  102   a - 102   n  and displaying at least one interest-based trait associated with the plurality of users, according to one illustrated embodiment. A hypothetical interest group membership vector  152  is determined at least in part based on the respective user interest group membership vectors  152   a - 152   n  associated with each of the users  102   a - 102   n  included in the first plurality of users. Using the data provided by the hypothetical interest group membership vector  152 , the run time subsystem  114  and/or the service provider  108  can determine one or more interest-related traits associated with the plurality of users  102   a - 102   n  included in the group. The method  1800  of determining a hypothetical user interest group membership vector  152  for a plurality of users  102   a - 102   n  and displaying at least one interest-based trait associated with the plurality of users commences at  1802 . 
     At  1804 , the evaluation system  110  generates a hypothetical user interest group membership vector  152  using at least some of the individual user interest group membership vectors  152   a - 152   n  associated with each of the users  102   a - 102   n  included in a plurality of the users. In some instances, some or all of the users  102   a - 102   n  selected for inclusion in the first plurality of the users may be autonomously determined, for example by the service provider  108 . In some instances, such autonomous selection may be based on one or more non-interest related traits, such as a user&#39;s sex, age, income, geographic location, education, profession, and similar. In some instances, some or all of the users  102   a - 102   n  selected for inclusion in the plurality of the users may be manually selected, for example based on the receipt by the service provider  108  of a user&#39;s interest in being included in the plurality of the users  102   a - 102   n . The hypothetical user interest group membership vector  152  logically associated with the plurality of the users  102   a - 102   n  may be determined using at least one of a mean or a median of the user interest group membership vectors  152   a - 152   n  of the users  102   a - 102   n  included in the first plurality of the users. 
     At  1806 , the evaluation system  110  determines one or more interest-related traits associated with the users  102   a - 102   n  included in the plurality of the users. Such analysis may, for example, determine that users included in the plurality of users based on a geographic location (e.g., Portland, Oreg.) have strong interests in outdoor-related backcountry activities. The method  1800  of determining a hypothetical user interest group membership vector  152  for a plurality of users  102   a - 102   n  and displaying at least one interest-based trait associated with the plurality of users concludes at  1808 . 
       FIG. 19  shows a high-level method  1900  of assessing the compatibility or similarity between a first plurality of the users that share a first non-interest related trait and a second plurality of the users that share a second non-interest related trait that is different than the first non-interested related trait based at least in part on respective hypothetical interest group membership vectors for the first plurality of the users and the second plurality of the users, according to one illustrated embodiment. A first hypothetical interest group membership vector  152 ′ is determined at least in part based on the respective user interest group membership vectors  152   a ′- 152   n ′ associated with the users  102   a ′- 102   n ′ included in the first plurality of users. The first plurality of users share a first non-interest related trait. A second hypothetical interest group membership vector  152 ″ is determined at least in part based on the respective user interest group membership vectors  152   a ″- 152   n ″ associated with the users  102   a ″- 102   n ″ included in the second plurality of users. The second plurality of users share a second non-interest related trait that is different than the first non-interest related trait. 
     Such a method may be used, for example, to compare hypothetical user interest group membership vector for a first plurality of users (in Vancouver, BC (the first non-interest related trait) with the hypothetical user interest group membership vector for a second plurality of users in Los Angeles, Calif. (the second non-interest related trait). The first and the second hypothetical interest group membership vectors  152 ′ and  152 ″ are compared and a level of similarity or dissimilarity assessed. The method  1900  of assessing the compatibility or similarity between a first plurality of the users  102   a ′- 102   n ′ and a second plurality of the users  102   a ″- 102   n ″ based on hypothetical interest group membership vectors for each of the first plurality of the users and the second plurality of the users commences at  1902 . 
     At  1904 , the evaluation system  110  generates a first hypothetical user interest group membership vector  152 ′ using at least some of the individual user interest group membership vectors  152   a ′- 152   n ′ of the users  102   a ′- 102   n ′ included in the first plurality of the users. The users  102   a ′- 102   n ′ included in the first plurality of users share at least a first non-interest related trait. In at least one implementation, the first non-interest related trait may include one or more geographic indicators (e.g., Vancouver, BC). In some instances, some or all of the users  102   a ′- 102   n ′ selected for inclusion in the first plurality of the users may be autonomously determined, for example by the service provider  108 . In some instances, some or all of the users  102   a ′- 102   n ′ selected for inclusion in the first plurality of the users may be manually selected, for example based on the receipt by the service provider  108  of a user&#39;s interest in being included in the first plurality of the users  102   a ′- 102   n ′. The hypothetical user interest group membership vector  152 ′ logically associated with the first plurality of the users  102   a ′- 102   n ′ may be determined using at least one of a mean or a median of the user interest group membership vectors  152   a ′- 152   n ′ of the users  102   a ′- 102   n ′ included in the first plurality of the users. 
     At  1906 , the evaluation system  110  generates a second hypothetical user interest group membership vector  152 ″ using at least some of the individual user interest group membership vectors  152   a ″- 152   n ″ of the users  102   a ″- 102   n ″ included in the second plurality of the users. The users  102   a ″- 102   n ″ included in the second plurality of users share at least a second, non-interest related, trait. In at least one implementation, the second non-interest related trait may be different than the first non-interest related trait and may include one or more geographic indicators (e.g., Austin, Tex.). In some instances, some or all of the users  102   a ″- 102   n ″ selected for inclusion in the second plurality of the users may be autonomously determined, for example by the service provider  108 . In some instances, some or all of the users  102   a ″- 102   n ″ selected for inclusion in the second plurality of the users may be manually selected, for example based on the receipt by the service provider  108  of a user&#39;s interest in being included in the first plurality of the users  102   a ″- 102   n ″. The hypothetical user interest group membership vector  152 ″ logically associated with the second plurality of the users  102   a ″- 102   n ″ may be determined using at least one of a mean or a median of the user interest group membership vectors  152   a ″- 152   n ″ of the users  102   a ″- 102   n ″ included in the second plurality of the users. 
     In some implementations, the first plurality of users  102   a ′- 102   n ′ and the second plurality of users  102   a ″- 102   n ″ may share a third, non-interest related, trait. For example, the third, non-interest related, trait may include a demographic trait (e.g., age, gender, income, education, occupation, and the like). In conjunction with a geographic first trait and second trait, such would allow comparing the similarities and/or differences between a first plurality of users sharing the third (demographic) trait (e.g., males) who also share a first geographic trait (e.g., in Vancouver, BC) with a second plurality of users also sharing the third trait (e.g., males) who also share the second geographic trait (e.g., in Miami, Fla.). 
     At  1908 , the user interest matching module  160  assesses the compatibility of the first plurality of users with the second plurality of users by comparing the first hypothetical user interest group membership vector  152 ′ with the second user interest group membership vector  152 ″. In some instances, by determining the similarity between the two hypothetical user interest group membership vectors, the user interest matching module  160  is able to quantify a degree of similarity or degree of difference existent between the first plurality of the users  102   a ′- 102   n ′ and the second plurality of the users  102   a ″- 102   n ″. Such information may be useful, for example in assisting the service provider  108  in determining whether the interests of the first plurality of the users  102   a ′- 102   n ′ in a first geographic area would be similar or different than the interests of the second plurality of users  102   a ″- 102   n ″ in a second geographic area. 
     In some instances, the user interest matching module  160  assesses the compatibility of the first plurality of users  102   a ′- 102   n ′ with the second plurality of users  102   a ″- 102   n ″ by determining a value indicative of a similarity, such as a cosine similarity, between the first hypothetical user interest group membership vector  152 ′ and the second hypothetical user interest group membership vector  152 ″. The user interest matching module  160  generates a value indicative of the cosine similarity between two user interest group membership vectors  152 ′,  152 ″. The greater the cosine similarity value between two hypothetical user interest group membership vectors  152 ′,  152 ″, the greater the degree of similarity between the two vectors. 
     In other instances, the user interest matching module  160  assesses the compatibility of the first plurality of users  102   a ′- 102   n ′ with the second plurality of users  102   a ″- 102   n ″ by determining a value indicative of a distance, such as the Euclidean distance, between the first hypothetical user interest group membership vector  152 ′ and the second hypothetical user interest group membership vector  152 ″. The smaller the Euclidean distance value between the hypothetical user interest group membership vectors  152 ′,  152 ″, the lesser the distance and the greater the degree of similarity between the two vectors. The method  1900  of assessing the similarity or differences between a first plurality of the users and a second plurality of the users based on hypothetical interest group membership vectors for each of the first plurality of the users and the second plurality of the users concludes at  1910 . 
     The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, schematics, and examples. Insofar as such block diagrams, schematics, and examples contain one or more functions and/or operations, it will be understood by those skilled in the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, the present subject matter may be implemented via Application Specific Integrated Circuits (ASICs). However, those skilled in the art will recognize that the embodiments disclosed herein, in whole or in part, can be equivalently implemented in standard integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more controllers (e.g., microcontrollers) as one or more programs running on one or more processors (e.g., microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of ordinary skill in the art in light of this disclosure. 
     Those of skill in the art will recognize that many of the methods or algorithms set out herein may employ additional acts, may omit some acts, and/or may execute acts in a different order than specified. 
     In addition, those skilled in the art will appreciate that the mechanisms taught herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment applies equally regardless of the particular type of signal bearing media used to actually carry out the distribution. Examples of signal bearing media include, but are not limited to, the following: recordable type media such as floppy disks, hard disk drives, CD ROMs, digital tape, and computer memory. 
     The various embodiments described above can be combined to provide further embodiments. All of the commonly assigned US patent application publications, US patent applications, foreign patents, and foreign patent applications referred to in this specification and/or listed in the Application Data Sheet, including but not limited to U.S. Provisional Patent Application No. 61/976,296, filed Apr. 7, 2014 are incorporated herein by reference, in their entirety. These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure. 
     These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.