Patent Publication Number: US-9418119-B2

Title: Method and system to determine a category score of a social network member

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 14/224,315, filed Mar. 25, 2014, now U.S. Pat. No. 8,990,191, issued Mar. 24, 2015. 
    
    
     TECHNICAL FIELD 
     This application relates to the technical fields of software and/or hardware technology and, in one example embodiment, to system and method to determine a category score of a social network member. 
     BACKGROUND 
     An on-line social network may be viewed as a platform to connect people in virtual space. An on-line social network may be a web-based platform, such as, e.g., a social networking web site, and may be accessed by a use via a web browser or via a mobile application provided on a mobile phone, a tablet, etc. An on-line social network may be a business-focused social network that is designed specifically for the business community, where registered members establish and document networks of people they know and trust professionally. Each registered member may be represented by a member profile. A member profile may be represented by one or more web pages, or a structured representation of the member&#39;s information in XML (Extensible Markup Language), JSON (JavaScript Object Notation) or similar format. A member&#39;s profile web page of a social networking web site may emphasize employment history and education of the associated member. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       Embodiments of the present invention are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like reference numbers indicate similar elements and in which: 
         FIG. 1  is a diagrammatic representation of a network environment within which an example method and system to determine a category score of a social network member may be implemented; 
         FIG. 2  is block diagram of a system to generate and use a category score of a social network member, in accordance with one example embodiment; 
         FIG. 3  is a flow chart of a method to determine a category score of a social network member, in accordance with an example embodiment; 
         FIG. 4  is a flow chart of a method for utilizing a category score of a social network member, in accordance with an example embodiment; and 
         FIG. 5  is a diagrammatic representation of an example machine in the form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. 
     
    
    
     DETAILED DESCRIPTION 
     A method and system to generate category scores for the profiles of members in an on-line social network is described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of an embodiment of the present invention. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Similarly, the term “exemplary” is merely to mean an example of something or an exemplar and not necessarily a preferred or ideal means of accomplishing a goal. Additionally, although various exemplary embodiments discussed below may utilize Java-based servers and related environments, the embodiments are given merely for clarity in disclosure. Thus, any type of server environment, including various system architectures, may employ various embodiments of the application-centric resources system and method described herein and is considered as being within a scope of the present invention. 
     For the purposes of this description the phrase “an on-line social networking application” may be referred to as and used interchangeably with the phrase “an on-line social network” or merely “a social network.” It will also be noted that an on-line social network may be any type of an on-line social network, such as, e.g., a professional network, an interest-based network, or any on-line networking system that permits users to join as registered members. For the purposes of this description, registered members of an on-line social network may be referred to as simply members. 
     Each member of an on-line social network is represented by a member profile (also referred to as a profile of a member or simply a profile). A member profile may be associated with social links that indicate that member&#39;s connection to other members of the social network. A member profile may also include or be associated with comments or endorsements from other members of the on-line social network, with links to other network resources, such as, e.g., publications, etc. As mentioned above, an on-line social networking system may be designed to allow registered members to establish and document networks of people they know and trust professionally. Any two members of a social network may indicate their mutual willingness to be “connected” in the context of the social network, in that they can view each other&#39;s profiles, profile recommendations and endorsements for each other and otherwise be in touch via the social network. 
     The profile information of a social network member may include personal information such as, e.g., the name of the member, current and previous geographic location of the member, current and previous employment information of the member, information related to education of the member, information about professional accomplishments of the member, publications, patents, etc. The profile information of a social network member may also include information provided by the member&#39;s connections. Information provided by the member&#39;s connections may be, e.g., recommendations, endorsements and skills. The profile of a member may include several items or units of a profile. For example one unit of a profile may contain information about the member&#39;s education, while another unit may contain information about the member&#39;s current and past employment. 
     An on-line social networking system may be configured to maintain one or more labels or categories that could be associated with a member profile. These categories may indicate a member&#39;s area of engagement or profession (e.g., sales, engineering, management, etc.), a member&#39;s career stage (e.g., a student, a career climber, a career keeper, etc.), or any other status or tendency that may be ascribed to a member. For example, a member category may be a sales category, which indicates that a member is a sales professional. Recognizing that a member can be associated with a certain category may be useful in determining whether that member is to be targeted for a particular promotion, or whether that member is likely to engage in a certain behavior in the context of the on-line social network. For example, if it can be determined that a member is engaged professionally in sales or business development, it may be inferred that the member may be more likely to purchase a subscription to premium services offered in the in-line social networking system than other members, such as, e.g., engineers. Often times, a member&#39;s profile does not explicitly indicate that the member can be associated with a particular category. It may be beneficial to determine how likely it is that a member is associated with a certain category. A measure of likelihood that a member is associated with a certain category may be termed a category score for the member profile. 
     Method and system are described for generating respective category scores for member profiles in the context of an on-line social network. According to one example embodiment, the data contained in and associated with a member profile may be examined and processed to generate a value with respect to a particular category that may be viewed as indicative of a member profile being associated with that particular category. The method operates by examining phrases contained in and associated with a member profile, determining whether any of these phrases have been given a weight value, and calculating the category score for the member profile based on the weight values of phrases from the member profile. The phrases and their associated weight values, referred to as weighted phrases or, collectively, a bank of weighted phrases, may be stored in a database maintained by or accessible by the on-line social networking system. 
     In some embodiments, a category score for a member profile may be generated using the member&#39;s social data—information from the member profiles of those members who are connections of the subject member. For example, if a member profile contains little information, e.g., very few phrases that describe the associated subject member, a category score for such member profile may be generated using the member&#39;s social data. In one embodiment, a category score for a member profile may be adjusted based on category scores of the member&#39;s connections. 
     A category score for a member profile may also be generated by taking into account behavior data of a member represented by the member profile. The behavior data of the member reflects activities of the member in the on-line social network system. A score generated based on the weight values of phrases from the member profile may be then termed definition score and a score generated based on the member&#39;s behavior data may be termed propensity score. A category score for a member profile may be generated as a combination of the definition score and the propensity score. 
     A bank of weighted phrases for a particular category, termed a target category, may be created using a variety of approaches. For example, a plurality of weighted phrases may be generated by a based on a seed sample of member profiles from the on-line social networking system. First, a random sample (e.g., ten thousand randomly-picked profiles) may be first selected. Then, a category scoring system may extract phrases from those randomly-picked profiles for each phrase from the plurality phrases and generate respective weight values for some or for all phrases, based on all phrases present in the random sample of member profiles and on the target category and, possibly on other factors. 
     The weighted phrases can be stored in a database as associated with the target category and then used for determining the likelihood of a member profile being associated with the target category. For example, if the target category is “sales/development,” the collection of weighted phrases associated with the “sales/development” category may be used to generate a numeric value—a category score—for a member profile reflecting the likelihood that the associated member is a sales or development professional. 
     In another embodiment, a seed sample of member profiles may be selected not randomly, but according to a certain criteria. Using the scenario above, where the target category is “sales/development,” the member profiles of those members who have purchased a subscription to a service provided by the on-line social networking system are given the “sales/development” label and are presumed to be associated with the “sales/development” category. The phrases that appear most commonly in the seed sample of profiles are assigned a higher weight value, while the phrases that appear less frequently in the seed sample of profiles are assigned a lower weight value. For example, phrases like “sales representative” and “business development” may appear in a large portion of the seed profiles and those phrases would be given a higher weight value with respect to the “sales/development” category. Phrases like “student” and “cloud computing” may appear in very few or none of the seed profiles and those would be given a negative weight value with respect to the “sales/development” category, because members that have these phrases appear in their profiles are rarely sales representatives or business development professionals. Some phrases, like, e.g., “great” or “well done” may be regarded as neutral with respect to a target category and be given a zero value, as they contribute no indication of whether the member profile can be associated with the “sales/development” category. Same phrases that appear in different fields of a member profile may be treated as different phrases. In one embodiment, respective weight values may be calculated for phrases in the seed profiles using one of the existing machine learning algorithms, such as, e.g., regularized linear regression, support vector machine (SVM), Naïve Bayes, etc. A matrix may be constructed based on the data stored in the seed profiles and a value may be generated for each user/phrase pair. The data stored in the seed profiles may include information such as title, education information, position, summary, patents, projects, publications, company, channel etc. An example matrix generated for determining weight values for phrases in the seed profiles is shown in Table 1 below. 
     
       
         
           
               
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 title: business 
                 skill: cloud 
                   
                   
               
               
                   
                 development 
                 computing 
                 . . .  
                 group: sales 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 user 1 
                 x11 
                 x12 
                   
                 x1m 
               
               
                 user 2 
                 x21 
                 x22 
                   
                 x2m 
               
               
                 user n 
                 xn1 
                 xn2 
                   
                 xnm 
               
               
                   
               
            
           
         
       
     
     There are different ways to assign values to xij {i=1:n, j=1:m}.
         Binary—1/0   Count—count the number of occurrences   TF/IDF (term frequency-inverse document frequency)       

     The dimensions of the matrix can be further expanded by adding user behavior and social features such as connections, page views, number of connections whose title contain a certain term, such as, e.g., “sales,” etc. 
     An example method and system for generating category scores for a social network member may be implemented in the context of a network environment  100  illustrated in  FIG. 1 . As shown in  FIG. 1 , the network environment  100  may include client systems  110  and  120  and a server system  140 . The client system  120  may be a mobile device, such as, e.g., a mobile phone or a tablet. The server system  140 , in one example embodiment, may host an on-line social network system  142 . As explained above, each member of an on-line social network is represented by a member profile that contains personal and professional information about the member and that may be associated with social links that indicate the member&#39;s connection to other member profiles in the on-line social network. Member profiles and related information may be stored in a database  150  as profiles  152 . 
     The client systems  110  and  120  may be capable of accessing the server system  140  via a communications network  130 , utilizing, e.g., a browser application  112  executing on the client system  110 , or a mobile application executing on the client system  120 . The communications network  130  may be a public network (e.g., the Internet, a mobile communication network, or any other network capable of communicating digital data). As shown in  FIG. 1 , the server system  140  also hosts a category scoring system  144  and a targeting system  146 . In one example embodiment, the category scoring system  144  is configured to examine profile information of a member and generate a category score for a member profile with respect to a target category, utilizing a bank of weighted phrases associated with the target category. The weighted phrases may be stored in the database  150  as weighted phrases  154 . The targeting system  146  may be part of or cooperate with the category scoring system  144 . The targeting system  146  may be utilized to examine member profiles stored in the database  150  to determine whether a particular member profile should be targeted for a certain purpose (e.g., advertisement or a job suggestion, etc.) based on a category score associated with a certain category determined for that member profile. 
     As mentioned above, a category score may be indicative of the member&#39;s professional and/or academic status and may be represented by a numerical value, e.g., a number between 1 and 10. The category scoring system  144  may be configured to perform the weighting of phrases, e.g., by analyzing phrases present in randomly or non-randomly selected group of member profiles. Alternatively, or additionally, the category scoring system  144  may be configured to obtain a collection of weighted phrases for a certain target category from another utility application, which may be provided by or executing at a server computer system of a third party actor, e.g., at a third party server system  160 . An example category scoring system  144  is illustrated in  FIG. 2 . 
       FIG. 2  is a block diagram of a system  200  to generate and use category scores for members of an on-line social network, in accordance with one example embodiment. As shown in  FIG. 2 , the system  200  includes a sample selector  210 , a weight value module  220 , a storing module  230 , an access module  240 , and a category score module  250 . 
     The sample selector  210  may be configured to select a sample of member profiles from the profiles maintained by an on-line social network system. As explained above, a member profile represents a member of the on-line social network system. The weight value module  220  may be configured to obtain, for at least some phrases present in the random sample of member profiles, respective weight values for a target category. In one embodiment, a weight value of a phrase may be calculated based on phrases present in the sample of member profiles and also utilizing the target category. The on-line social network system may maintain one or more member categories (or category labels), such as, e.g., a “potential subscriber” category, a “sales professional” category, an “executive” category, etc. A phrase may have a certain weight value with respect to one category and a different weight with respect to another category. For example, with respect to a “sales professional” category the phrase “sales and marketing” may have a higher weight value than with respect to an “engineering” category. Conversely, the phrase “software development” may have a higher weight value with respect to an “engineering” category than with respect to a “nursing” category. 
     A combination of a phrase and its weight value may be termed a weighted phrase. The phrases together with their respective weight values with respect to a category may be termed weighted phrases. The storing module  230  may be configured to store the plurality of weighted phrases in a database. 
     The access module  240  may be configured to access a member profile from the on-line social network system and also access the plurality of weighted phrases associated with a certain category (a target category), in order to start the process of determining a category score for that member profile with respect to the target category. The category score module  250  may be configured to determine a category score for the member profile based on a presence of one or more phrases from the plurality of weighted phrases in the member profile. As explained above, the category score indicates the likelihood of the member profile being associated with the target category. 
     As mentioned above, the weight value module  220  may be configured to obtain respective weight values for phrases with respect to a category. In some embodiments, the obtaining of the weight values for phrases with respect to a category may include generating said weight values. The weight value module  220  may thus be configured to extract the plurality of phrases from a sample of member profiles and to calculate a weight value for each phrase from the plurality phrases. 
     A sample of member profiles that is used for obtaining phrases and generating their respective values—the so-called seed profiles—may be not random, but selected based on a predetermined criteria. For example, the predetermined criteria may be based on a particular activity or behavior of a member in the context of the on-line social network system. One such criterion may be the action of purchasing a subscription to a service provided by the on-line social networking system. For example, where the target category is “potential subscriber,” the member profiles of those members who have purchased a subscription to a service provided by the on-line social networking system are given the “potential subscriber” label and are presumed to be associated with the “potential subscriber” category. The phrases that appear most commonly in the seed sample of profiles are assigned a higher weight value, while the phrases that appear less frequently in the seed profiles are assigned a lower weight value. For example, phrases like “sales representative” and “business development” may appear in a large portion of the seed profiles and those phrases would be given a higher weight value with respect to the “potential subscriber” category. 
     In other embodiments, the obtaining of the weight values for phrases with respect to a category may include providing the plurality of phrases from a sample of member profiles—that were selected randomly or according to a predetermined criteria—to a further computer system, e.g., to a third party computer system. The processing of the phrases found in the sample of member profiles may be performed at the further computer system, e.g., at the third party server system  160  of  FIG. 1 . The weight value module  220  may then receive the plurality of weighted phrases from the further computer system. 
     The category score module  250  may be configured to determine, based on a presence of one or more phrases from the plurality of weighted phrases in a member profile, a category score for that member profile, where the category score indicates the likelihood of the member profile being associated with the target category. In one embodiment, the category score module  250  may also be configured to access behavior data of a member represented by the member profile, generate propensity score for the member profile based on the behavior data of the member, and utilize the propensity score in generating the category score for the member profile. The behavior data of the member reflects activities of the member in the on-line social network system. Where the behavior data of a member is used in generating a category score, the category score module  250  may be configured to generate a score for the member profile based on phrases that are present in the member profile (termed definition score), generate a score for the member profile based on behavior data of a member represented by the member profile (termed propensity score), and combine the definition score and the propensity score to generate a category score for the member profile. In one embodiment, a definition score is determined by accessing a plurality of weighted phrases stored in a database, determining respective weights for phrases present in the member profile utilizing the plurality of weighted phrases, and combining the respective weights for phrases present in the member profile to produce the definition score. As described above, a category score associated with a “potential subscribers” category may indicate the likelihood that the member becomes a subscriber to a service provided by the on-line social network system. In this scenario a category score may be referred to as a subscriber score. 
     The category score module  250  may also be configured to use social data of a member represented by a member profile in generating a category score. For example, the category score module  250  may be configured to access the profile of a member who is a connection of the member represented by the member profile, for which a category score is being determined, utilize a category score of that member profile for generating the category score for the member profile. The category score module  250  may be configured to adjust the category score of the member profile based on the category score of the profile of the member&#39;s connection or to assign the category score of the profile of the member&#39;s connection to the member profile. The storing module  230  may be configured to store the category score of the member profile as associated with the member profile, e.g., in the database  150  of  FIG. 1 . 
     Also shown in  FIG. 2  are a member segmentation module  260  and a communications module  270 . The member segmentation module  260  may be configured to retrieve, from a database, the category score for the member profile; compare the category score for the member profile to a threshold value; and, based on a result of the comparing, selectively identify the member profile as associated with the target category. The communications module  270  may be configured to selectively send a communication to the member based on a result of the comparing, responsive to the member profile having been identified as associated with the target category. For example, where the category scoring system  144  is utilized to determine which members of the on-line network system  142  are likely to be interested in purchasing a subscription to a service provided by the on-line network system  142 , the target category may be labeled “potential subscribers.” When the category scoring system  144  identifies a member profile as likely to represent a member who is a potential subscriber, based on the calculated category score, the communications module  270  may send an email to the member represented by the member profile inviting the member to subscribe to a service provided by the on-line network system  142  and, optionally, offer a subscription discount. 
     The modules shown in  FIG. 2  may represent the modules included in the category scoring system  144  and the targeting system  146 , both of  FIG. 1 . The sample selector  210 , the weight value module  220 , the storing module  230 , the access module  240 , and the category score module  250  may be part of the category scoring system  144  of  FIG. 1 . The member segmentation module  260  and the communications module  270  may be part of the targeting system  146 . As mentioned above, all of the modules shown in  FIG. 2  may be part of a single system that combines the functionality of both the category scoring system  144  and the targeting system  146  of  FIG. 1 . 
       FIG. 3  is a flow chart of a method  300  to generate category scores for members of an on-line social network, according to one example embodiment. The method  300  may be performed by processing logic that may comprise hardware (e.g., dedicated logic, programmable logic, microcode, etc.), software (such as run on a general purpose computer system or a dedicated machine), or a combination of both. In one example embodiment, the processing logic resides at the server system  140  of  FIG. 1  and, specifically, at the system  200  shown in  FIG. 2 . 
     As shown in  FIG. 3 , the method  300  commences at operation  310 , when the sample selector  210  of  FIG. 2  selects a random sample of member profiles from the profiles  152  maintained by the on-line social network system  142  of  FIG. 1 . The weight value module  220  of  FIG. 2  obtains, for at least some phrases present in the random sample of member profiles, respective weight values for a target category, at operation  320 . As explained above, the obtaining of respective weight values for phrases with respect to a category may include generating said weight values. The obtaining of respective weight values for phrases with respect to a category may include providing the plurality of phrases from a sample of member profiles—that were selected randomly or according to a predetermined criteria—to a further computer system, e.g., to a third party computer system, such as, e.g., to the third party server system  160  of  FIG. 1  and receiving the plurality of weighted phrases from the further computer system. At operation  330 , the storing module  230  stores the plurality of weighted phrases in a database, e.g., in the database  150  of  FIG. 1 . 
     At operation  340 , the access module  240  of  FIG. 2  accesses a member profile from the on-line social network system  142  and also access the plurality of weighted phrases associated with a target category, in order to start the process of determining a category score for that member profile with respect to the target category. At operation  350 , the category score module  250  of  FIG. 2  determines, based on a presence of one or more phrases from the plurality of weighted phrases in a member profile, a category score for that member profile. In one embodiment, the category score may be determined utilizing propensity score for the member profile. Propensity score may be generated based on the behavior data of the member represented by the member profile. 
       FIG. 4  is a flow chart of a method  400  for utilizing category scores, according to one example embodiment. The method  400  may be performed by processing logic that may comprise hardware (e.g., dedicated logic, programmable logic, microcode, etc.), software (such as run on a general purpose computer system or a dedicated machine), or a combination of both. In one example embodiment, the processing logic resides at the server system  140  of  FIG. 1 . 
     As shown in  FIG. 4 , the method  400  commences at operation  410 , when the access module  240  of  FIG. 2  accesses a member profile from the on-line social network system  142  and also access the plurality of weighted phrases associated with a target category, in order to start the process of determining a category score for that member profile with respect to the target category. At operation  420 , the category score module  250  of  FIG. 2  determines, based on a presence of one or more phrases from the plurality of weighted phrases in a member profile, definition score for that member profile. At operation  430 , the category score module  250  generates propensity score for the member profile based on behavior data of the member represented by the member profile. A category score is generated for the member profile based on both the definition score and the propensity score, at operation  440 . As mentioned above, a category score that indicates the likelihood of the member becoming a subscriber to a service provided by the on-line social network system a category score may be referred to as a subscriber score. 
     At operation  460 , the member segmentation module  260  compares the category score of the member profile to a threshold value. The communications module  270  selectively sends a communication to the member based on a result of the comparing, at operation  470 . 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
       FIG. 5  is a diagrammatic representation of a machine in the example form of a computer system  700  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a stand-alone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  700  includes a processor  702  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory  704  and a static memory  706 , which communicate with each other via a bus  707 . The computer system  700  may further include a video display unit  710  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  700  also includes an alpha-numeric input device  712  (e.g., a keyboard), a user interface (UI) navigation device  714  (e.g., a cursor control device), a disk drive unit  716 , a signal generation device  718  (e.g., a speaker) and a network interface device  720 . 
     The disk drive unit  716  includes a machine-readable medium  722  on which is stored one or more sets of instructions and data structures (e.g., software  724 ) embodying or utilized by any one or more of the methodologies or functions described herein. The software  724  may also reside, completely or at least partially, within the main memory  704  and/or within the processor  702  during execution thereof by the computer system  700 , with the main memory  704  and the processor  702  also constituting machine-readable media. 
     The software  724  may further be transmitted or received over a network  726  via the network interface device  720  utilizing any one of a number of well-known transfer protocols (e.g., Hyper Text Transfer Protocol (HTTP)). 
     While the machine-readable medium  722  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing and encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of embodiments of the present invention, or that is capable of storing and encoding data structures utilized by or associated with such a set of instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media. Such media may also include, without limitation, hard disks, floppy disks, flash memory cards, digital video disks, random access memory (RAMs), read only memory (ROMs), and the like. 
     The embodiments described herein may be implemented in an operating environment comprising software installed on a computer, in hardware, or in a combination of software and hardware. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is, in fact, disclosed. 
     Modules, Components and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein. 
     In various embodiments, a hardware-implemented module may be implemented mechanically or electronically. For example, a hardware-implemented module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware-implemented module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware-implemented module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term “hardware-implemented module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily or transitorily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware-implemented modules are temporarily configured (e.g., programmed), each of the hardware-implemented modules need not be configured or instantiated at any one instance in time. For example, where the hardware-implemented modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware-implemented modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware-implemented module at one instance of time and to constitute a different hardware-implemented module at a different instance of time. 
     Hardware-implemented modules can provide information to, and receive information from, other hardware-implemented modules. Accordingly, the described hardware-implemented modules may be regarded as being communicatively coupled. Where multiple of such hardware-implemented modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware-implemented modules. In embodiments in which multiple hardware-implemented modules are configured or instantiated at different times, communications between such hardware-implemented modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware-implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware-implemented module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware-implemented modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).) 
     Thus, a method and system to determine a category score of a social network member has been described. Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.