Abstract:
There is disclosed a method of facilitating interaction between users of an electronic community. In an embodiment, the method comprises: reviewing a user activity log for each user in the electronic community; executing a natural language parser to extract significant noun phrases from the user activity log; updating user profiles from the newly extracted noun phrases, based on their usage frequency and importance value; and storing the updated profiles in a user profile and relationship data base; and executing a similarity based clustering algorithm to cluster user profiles, thereby discovering relationships among users and storing them in a user profile and relationship database. The method may further comprise displaying for each user the one or more relationships to which the user is assigned, together with a list of users assigned to the one or more relationships. The method may also comprise storing for each user the relationship to which the user is assigned in a user profile and relationship database.

Description:
FIELD OF THE INVENTION 
     The invention relates to a system and method for interaction between users, particularly users of an online community, such as a social network. More generally, the system and method automatically discovers potential relationships which may facilitate more interaction between users. 
     BACKGROUND OF THE INVENTION 
     Online communities, such as social networking sites, continue to grow in popularity as they allow participants to safely interact with other participants in a virtual environment. However, the potential for interaction between the various participants may not be readily apparent, and this may limit the actual interactions that take place between the participants. Some prior art solutions have attempted to define potential relationships between individuals. However, this has been based on limited analysis of user activity or self-identification through surveys, resulting in mapping of only simple connections between users. 
     SUMMARY OF THE INVENTION 
     The present invention relates to a system and method for interaction between users, particularly users of an online community, such as a social network. More generally, the system and method automatically discovers potential relationships which may facilitate more interaction between users. Automatically discovering the potential for increased interaction between users may have significant added value, both in terms of business opportunities for the site operator due to increased usage, and in terms of usefulness and effectiveness of the online social networking site for the users. 
     In an embodiment, a system and method in accordance with the present invention automatically generates user-interest profiles for each user of an online community, such as an online social network. The users are classified into different interest groups, and then the potential user relationships are displayed using various linking tools, such as hyperlinks in the case of web page interfaces, for example. The system and method may also gather information about whom and how individual users interact with other users and systems. Unlike conventional data gathering techniques, the system and method in accordance with an embodiment of the present invention clusters keywords in user log data in order to extrapolate and identify interconnections or relationships that may result in potential interactions between users. 
     In an aspect, there is provided a method of facilitating interaction between users of an electronic community, comprising: reviewing a user activity log for each user in the electronic community; executing a natural language parser to extract significant noun phrases from the user activity log; updating user profiles from the newly extracted noun phrases, based on their usage frequency and importance value; and storing the updated profiles in a user profile and relationship data base; and executing a similarity based clustering algorithm to cluster user profiles, thereby discovering relationships among users and storing them in a user profile and relationship database. 
     In an embodiment, the method further comprises displaying for each user the one or more relationships to which the user is assigned, together with a list of users assigned to the one or more relationships. 
     In another embodiment, the method further comprises storing for each user the one or more relationships to which the user is assigned in a user profile and relationship database. 
     In another embodiment, the method further comprises displaying the one or more relationships together with a list of users. 
     In another embodiment, the method further comprises providing a user interface for modifying the user profile in the user profile and relationship database, such that a user may manually add or remove the keywords and modify the weights of the keywords. 
     In another embodiment, the method further comprises: updating the user profiles from user activity logs at regular intervals; re-executing the similarity based clustering algorithm on the updated user profiles at regular intervals; displaying any newly assigned relationships to which the user is assigned, together with a list of users assigned to the newly assigned relationships; and removing any relationships to which the user is no longer assigned. 
     In another embodiment, the method further comprises providing a user interface for limiting the number of relationships displayed, and the number of users displayed for each relationship. 
     In another aspect, there is provided a system for facilitating interaction between users of an electronic community, comprising: means for reviewing a user activity log for each user in the electronic community; means for executing a natural language parser to extract significant noun phrases from the user activity log; means for updating user profiles from the newly extracted noun phrases, based on their usage frequency and importance value; and storing the updated profiles in a user profile and relationship data base; and means for executing a similarity based clustering algorithm to cluster user profiles, thereby discovering relationships among users and storing them in a user profile and relationship database. 
     In an embodiment, the system further comprises means for displaying for each user one or more relationships to which the user is assigned, together with a list of users assigned to the one or more relationships. 
     In another embodiment, the system further comprises means for storing for each user the one or more relationships to which the user is assigned in a user profile and relationship database. 
     In another embodiment, the system further comprises means for displaying the one or more relationships together with a list of users. 
     In another embodiment, the system further comprises means for providing a user interface for modifying the user profile in the user profile and relationship database, such that a user may manually add or remove the keywords and modify the weights of the keywords. 
     In another embodiment, the system further comprises: means for updating the user profiles from user activity logs at regular intervals; means for re-executing the similarity based clustering algorithm on the updated user profiles at regular intervals; a display for displaying any newly assigned relationships to which the user is assigned, together with a list of users assigned to the newly assigned relationships; and means for removing any relationships to which the user is no longer assigned. 
     In another embodiment, the system further comprises means for providing a user interface for limiting the number of relationships displayed, and the number of users displayed for each relationship. 
     In another aspect, there is provided a data processor readable medium storing data processor code that when loaded into a data processor device adapts the device to perform a method of facilitating interaction between users of an electronic community, comprising: code for reviewing a user activity log for each user in the electronic community; code for executing a natural language parser to extract significant noun phrases from the user activity log; code for updating user profiles from the newly extracted noun phrases, based on their usage frequency and importance value; and storing the updated profiles in a user profile and relationship data base; and code for executing a similarity based clustering algorithm to cluster user profiles, therefore discovering relationships among users and storing them in a user profile and relationship database. 
     In an embodiment, the data processor readable medium further comprises code for displaying for each user the one or more relationships to which the user is assigned, together with a list of users assigned to the one or more relationships. 
     In another embodiment, the data processor readable medium further comprises code for storing for each user the one or more relationships to which the user is assigned in a user profile and relationship database. 
     In another embodiment, the data processor readable medium further comprises code for displaying the one or more relationships together with a list of users. 
     In another embodiment, the data processor readable medium further comprises code for providing a user interface for modifying the user profile in the user profile and relationship database, such that a user may manually add or remove the keywords and modify the weights of the keywords. 
     In another embodiment, the data processor readable medium further comprises: code for updating the user profiles from user activity logs at regular intervals; code for re-executing the similarity based clustering algorithm on the updated user profiles at regular intervals; code for displaying any newly assigned relationships to which the user is assigned, together with a list of users assigned to the newly assigned relationships; and code for removing any relationships to which the user is no longer assigned. 
     These and other aspects of the invention will become apparent from the following more particular descriptions of exemplary embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the figures which illustrate exemplary embodiments of the invention: 
         FIG. 1  shows a generic data processing system that may provide a suitable operating environment; 
         FIG. 2  shows a schematic block diagram of an illustrative system in accordance with an embodiment; 
         FIG. 3  shows an illustrative method in accordance with an embodiment; 
         FIG. 4  shows another illustrative method in accordance with an embodiment; 
         FIG. 5  shows another illustrative method in accordance with an embodiment; and 
         FIG. 6  shows yet another illustrative method in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     As noted above, the present invention relates to systems and methods for interactions between users of an online community, such as an online social network. 
     The invention may be practiced in various embodiments. A suitably configured data processing system, and associated communications networks, devices, software and firmware may provide a platform for enabling one or more embodiments. By way of example,  FIG. 1  shows a generic data processing system  100  that may include a central processing unit (“CPU”)  102  connected to a storage unit  104  and to a random access memory  106 . The CPU  102  may process an operating system  101 , application program  103 , and data  123 . The operating system  101 , application program  103 , and data  123  may be stored in storage unit  104  and loaded into memory  106 , as may be required. An operator  107  may interact with the data processing system  100  using a video display  108  connected by a video interface  105 , and various input/output devices such as a keyboard  110 , mouse  112 , and disk drive  114  connected by an I/O interface  109 . In known manner, the mouse  112  may be configured to control movement of a cursor in the video display  108 , and to operate various graphical user interface (GUI) controls appearing in the video display  108  with a mouse button. The disk drive  114  may be configured to accept data processing system readable media  116 . The data processing system  100  may form part of a network via a network interface  111 , allowing the data processing system  100  to communicate with other suitably configured data processing systems (not shown). The particular configurations shown by way of example in this specification are not meant to be limiting. 
     Now referring to  FIG. 2 , shown is an illustrative system  200  in accordance with an embodiment. As shown, system  200  may include an activity log server  202  which logs a user&#39;s interactions within the system  200 , including the user&#39;s interaction with various system modules, and with other users of system  200 . Many kinds of user activities (e.g. email, voicemail, voice conversations, instant messages, and electronically stored files, among others) can be logged, and converted into text format as necessary for the purposes of clustering noun phrases or keywords extracted from user log data, in order to extrapolate and identify potential interconnections or relationships. 
     The activity log server  202  may be operatively connected to an intelligent profile updater  204  which may be configured to update the user&#39;s profile based on recent user activity, as logged on activity log server  202 . The intelligent profile updater  204  may also be configured to check the user activity logs periodically (e.g. daily, hourly, weekly), and to analyze the user activity logs with the help of a language parsing engine  206  and a digital hierarchical dictionary  212 . A digital hierarchical dictionary, such as WORDNET®, groups noun phrases into sets of cognitive synonyms (“synsets”), each expressing a distinct concept. These synsets are then interlinked into a semantic hierarchical tree by means of conceptual-semantic and lexical relations. The intelligent profile updater  204  may further be operatively connected to a user profile and relationship database  208 . The natural language parsing engine  206  may be used to calculate the meaningfulness of noun phrases or keywords, and to extract meaningful noun phrases or keywords for constructing a user profile. The constructed user profile may then be stored in the user profile and relationship database  208 . 
     User profile and relationship database  208  may in turn be operatively connected to a clustering algorithm module  210  which may be used to cluster users into different and possibly overlapping context groups. The clustering algorithm module  210  may also be operatively connected to digital hierarchical dictionary  212  for calculating similarity distances between the noun phrases or keywords. 
     Still referring to  FIG. 2 , the user profile &amp; relationship database  208  may be operatively connected to a front-end user relationship display  214  which may be adapted to display the relationship between users or user groups. For example, in a web based application interface, these relationships may be shown using hyperlinks. The relationships may also be displayed using various other means, such as graphical connectors linking two or more users. User profile and relationship database  208  may also be operatively connected to a front-end manual profile updater  216 , which may be adapted to allow users to manually update their own profiles and override any potential relationship links calculated and assigned by the system  200 . 
     As will be further explained below, in this system  200 , the discovered relationships between users are not static. Rather, the relationships may continue to evolve as the users&#39; interests change over time. New potential relationships may form between users and old relationships may disappear, as explained further below. 
     Now referring to  FIG. 3 , shown is an illustrative method  300  in accordance with an embodiment. As shown, method  300  begins at block  301 , where the user activities logged in the activity log server  202  are retrieved and input at block  301  for processing. 
     Next, method  300  proceeds to block  302 , where method  300  may review each user&#39;s end of day user activity log. While a daily review of a user&#39;s activity log takes place in this illustrative example, it will be appreciated that various other time periods may also be used for the purposes of analysis, such as weekly, or hourly for example. 
     Method  300  may then proceed to block  303 , where method  300  performs a text extraction from the user&#39;s activity logs. If the logged activity is not already in a text format (e.g. voicemail and recorded voice conversations), then the logged activity can be converted into suitable text format using various known speech-to-text conversion tools. 
     Method  300  may then proceed to block  304  where the text messages, which may have been translated into text from voice or other types of user activity logs, are parsed by a language parsing engine, such as natural language parsing engine  206  of  FIG. 2 . In an illustrative embodiment, the natural language processing engine used may be the General Architecture for Text Engineering (GATE) natural language engine. 
     Method  300  may then proceed to block  305 , where method  300  may employ the natural language parsing engine  206  to form a noun phrases vector containing 0 to n number of noun phrases. 
     Method  300  may then proceed to block  306 , where method  300  may map common names, or names not found in the digital hierarchical dictionary  212 , to dictionary defined noun phrases. For example, if “George Bush” is not in the digital hierarchical dictionary  212 , it may be mapped to a word or phrase in the digital hierarchical dictionary  212 , such as “politics” or “American politics”. The mapped word or phrase, for example, “American politics” may then be used for the keyword analysis. This mapping is provided by human and can be stored in a pure text file or in a database table before hand. It acts like an extension to the digital hierarchical dictionary. It is used in certain situations when the meaning of certain words and idioms are only known to certain audiences. In method  300 , this file or table is searched for locating the similar phrase in the digital hierarchical dictionary. 
     Method  300  may then proceed to block  307 , where method  300  may calculate the importance value of each new noun phrase or keyword, and remove the less meaningful ones. As an illustrative example, the importance value of each noun phrase or keyword may be decided by its depth in the semantic hierarchical tree of the online dictionary, WORDNET®. For example, in WORDNET, “bike” has a greater importance value than “vehicle” because bike is a specific type of vehicle, and is therefore more descriptive. 
     Method  300  may then proceed to block  308 , where the most important keywords left after block  307  form a keywords vector of 0 to n noun phrases. Method  300  may then proceed to block  309 , where the new keywords vector is applied to update a user profile. All the keywords in a user profile are weighted by their usage frequency by the user and importance value. It could be a brand new keyword that is added to the user profile including the weight of importance value. Or if a keyword is already in the user profile, its weight may be increased by the importance value of the instance of the keyword. The user profile also maintains a feature list, which contains a list of most highly weighted keywords. This feature list is then used by the clustering algorithm for user relationship discovery in method  400  as described further below. The feature list evolves every time the user profile is updated. A more important keyword can be added to the feature list while a less important one is thrown out. This changing feature list affects the relationship discovery process. As described earlier, new potential relationships may form while old relationships may disappear. Method  300  may then loop back to block  301 , and repeat as necessary. 
     Now referring to  FIG. 4 , shown is a method  400  for generating user-related processes in accordance with an embodiment. Method  400  starts at block  401  for all users. Method  400  then proceeds to block  402  where method  400  processes keywords using a member similarity based clustering algorithm module (e.g. clustering algorithm module  210 ). This clustering module classifies user profiles by using a member importance function and a member similarity function. In the present system, the member importance function is implemented as the depth in the semantic hierarchical tree of a dictionary. The member similarity function is then implemented as the path distance in the semantic hierarchical tree of a dictionary (e.g. digital hierarchical dictionary  212 ). The Clustering algorithm  402  allows the tuning of the clustering parameters for different effects. For example, we may adjust the similarity threshold to fine tune the number of clusters it returns. The bigger the similarity threshold, the less number of clusters (relationships); The smaller the similarity threshold, the more number of clusters (relationships). 
     Method  400  then proceeds to block  403 , where method  400  generates a list of interconnections for all users, each one containing a list of users, and a group of representative keywords for that group. 
     Method  400  then proceeds to block  404 , where method  400  calculates a relationship importance value based on the total sum of the importance values of its keywords that representing this relationship. 
     Method  400  then proceeds to block  405 , where method  400  stores these relationships in the user profile and relationship database (e.g. user profile and relationship database  208 ). In the database, each relationship has an importance value and a group of representative keywords, as well as a reference to a list of users in that group. The importance value represents the relative significance of this. The group of representative keywords are the outcome of the clustering algorithm (like block  210 ). The keywords are usually closely related in meaning and when clustered together, they also define the type of the relationship. For example, a relationship is represented by “George Bush, American Politics”. There could be active users on the topics of “George Bush, American Politics”, and they are included in this relationship. The keyword clustering method as summarized above is also described in greater detail in co-pending U.S. patent application Ser. No. 11/366,517. 
     Now referring to  FIG. 5 , shown is an illustrative example of a method  500  for displaying the user interconnections. This process is activated, for example, when a user logs in his/her application or web page. The backend process retrieves the relationship groups and users that are related to the current user, formats them, and displays them, for example as hyperlinks. The relationships shown can be filtered by the user&#39;s current log-on role and context. For example, the relationships shown when a user logs on at home are different from the relationships shown when a user logs on at work. 
     Method  500  begins at block  501  with a user login, and proceeds to block  502 , where a relationship display module is activated upon user access of an application or webpage. 
     Method  500  then proceeds to block  503 , where the user profile and relationship database is searched to retrieve all relationships which contain the user, up to a maximum size as defined by the user, the most important relationships being retrieved first. 
     Method  500  then proceeds to block  504 , where for each relationship retrieved in block  503 , the user profile and relationship database is searched for other users in that relationship, up to a maximum size as defined by the user. 
     Method  500  then proceeds to block  505 , where the relationship list as retrieved in blocks  503  and  504  is formatted, with each relationship and its users being interconnected, for example by hyperlink in a web page interface. 
     Now referring to  FIG. 6 , shown is a method  600  for managing a user profile. Method  600  begins with a user login at block  601  and proceeds to block  602 , where method  600  accesses the relationship profile section of the user. From block  602 , method  600  may proceed to any one of blocks  603  to  607 , where method  600  performs each of the following steps. 
     At block  603 , method  600  searches all relationships related to a keyword, so that a user knows the connections of a keyword to known relationships and the effects it can bring to the relationship building process by adding/removing this keyword to/from his/her profile. Method  600  then proceeds to block  604 , where method  600  modifies the user&#39;s keywords section (e.g. add, remove, modify, or move up or down in priority) so as to manually affect the relationship building process. Method  600  may also by-pass block  603  to go to block  604  directly. From block  604 , method  600  proceeds to block  608 , where the user exits his/her profile section. 
     At block  605 , method  600  may allow the user to turn the auto profiler and relationship display on or off. Method  600  may then proceed to block  608  where the user exits. 
     At block  606 , method  600  may allow the user to set the maximum number of relationships to which a user may be associated. Method  600  may then proceed to block  608  where the user may exit. 
     At block  607 , method  600  may allow the user to set the maximum number of users that can be displayed under any relationship. Method  600  may then proceed to block  608 , where the user may exit the profile section. 
     As will be appreciated, the user profile interface described above is meant to be interactive and configurable by the user to suit their needs. As well, the system in not static, but rather the user input can and will be used to influence the relationship group outcomes. For instance, the users can modify their own profiles so to manually affect the relationship generation process. For example, if the user has posted a message that has a word “rose” in it, but he doesn&#39;t want to be connected to people with a gardening interest, he/she can delete the keyword “rose” from his keyword profile. The profile can also be used to “discover” relationships in an area where the user has no electronic history based on the user&#39;s activity log. For instance if he has not had any electronic activities relating to “rose gardening”, but he wants to be connected to people with that interest, he can also manually add keywords to his keyword profile, which would cause the system to evaluate again, looking for new potential relationships. As well, the user can move the keywords to emphasis or de-emphasis an area. For example, if a user has lost interesting in rose gardening, the user may move those keywords down the priority list by reducing its weight. 
     The user can also limit the number of users that can be displayed under any discovered relationship. This can be done, for example, by displaying only the top five users and/or relationships. Similarly if the user is searching for more obscure topics, the user can specify relationships that fall in the range of the lower than 10 and higher than 25. 
     By managing the keywords, the users can expose, hide, and filter what information they or others see. In an embodiment, the user can also type in a keyword, and the system will present the keywords/clusters related to that keyword. This permits the ability to search a user&#39;s relationship map. For example, the keywords “pruning roses” may be typed in the system to show potential relationships and related keywords. In this manner, the user knows how she/he should handle that keyword in her/his profile. 
     While various illustrative embodiments of the invention have been described above, it will be appreciated by those skilled in the art that variations and modifications may be made. Thus, the scope of the invention is defined by the following claims.