Topic Based Recommender System & Methods

Abstract

A recommendation system is used to provide suggestions in environments such as message boards, RSS aggregators, blogs and the like by comparing member interests and creating recommendation items corresponding to categorized topics or other members. In some instances a natural language can assist in processing content to sort it into the appropriate topic bin. An advertising module cooperates with the system to provide content based ads relevant to the recommended items.

1 . A method of generating automatic recommendations for content to a first user with a computing system comprising:
 (a) identifying a first content reviewed by the first user with the computing system;   (b) identifying a second content reviewed by a plurality of second users with the computing system;   (c) causing a recommender system to generate a prediction and/or a recommendation for portions of said second content which are likely to be of interest to the first user based on an analysis of said first content and said second content;
 wherein said first content and second content includes materials derived and combined with the computing system as multidimensional data from at least two of the following content sources accessed by said first user and said plurality of second users: 1) a message board; 2) a social network site; 3) a blog; 4) an RSS feed; 5) a content site; 
 wherein the prediction and/or recommendation is based on multidimensional data. 
   
     
     
         2 . The method of  claim 1  wherein said recommender prediction and/or recommendation is further based on content authored by said first user and/or said plurality of second users. 
     
     
         3 . The method of  claim 1  where at least some of said content is derived from implicit ratings determined from classifying data reviewed by the first user and said plurality of second users into one or more topics or concepts. 
     
     
         4 . The method of  claim 1  wherein said prediction and/or a recommendation is based on explicit ratings provided by the first user and said plurality of second users. 
     
     
         5 . The method of  claim 4  wherein said explicit ratings are given a weighting in accordance with a time characteristic. 
     
     
         6 . The method of  claim 5  wherein weighting increases for older ratings. 
     
     
         7 . The method of  claim 5  wherein said weighting is also adjusted based on a frequency of ratings provided for a particular data item. 
     
     
         8 . The method of  claim 1  further including a step: presenting an advertisement along with said recommendation, which advertisement is based on a content of said recommendation. 
     
     
         9 . A method of generating automatic recommendations for content to a first user with a computing system comprising:
 (a) processing a set of first ratings from the first user for a first data source with the computing system, which first data source includes at least one of a human author, a social network contact, a message board, an RSS feed and/or a web log;   (b) processing a set of second ratings from one or more second users for said first data source and one or more second data sources with the computing system, which second data sources also include at least one of a human author, a social network contact, a message board, an RSS feed and/or a web log;   (c) correlating said set of first ratings and said set of second ratings with the computing system to identify a selected set of second users that are suitable as predictors for said first user;   (d) recommending one or more of said second data sources to said first user based on a correlation of said first user to said selected set of second users done with the computing system.   
     
     
         10 . The method of  claim 9  wherein said correlation is determined by at least one of collaborative filtering and/or corroborative filtering. 
     
     
         11 . The method of  claim 9  wherein said first set of ratings and said second set of ratings includes implicit ratings data which is determined implicitly from actions taken by said first user and said set of second users in reviewing content presented electronically during an Internet session. 
     
     
         12 . The method of  claim 9  wherein said set of first ratings and said set of second ratings are recommendations given to authors of message board posts. 
     
     
         13 . The method of  claim 9  further including a step: presenting an advertisement to said first user which contains content predicted based on said correlation. 
     
     
         14 . A method of generating automatic recommendations for content to a first user with a computing system comprising:
 (a) providing a database correlating a plurality of individual data items and rankings for a first user, wherein at least some of said individual data items represent human individuals;   (b) identifying first content presented to the first user with the computing system;   (c) identifying a first rating provided by said first user with the computing system for said first content which is related to least a first one of said plurality of individual data items;   (d) identifying second content presented to said first user with the computing system;   (e) identifying a second rating provided by said first user with the computing system for said second content which is related to least a second one of said plurality of individual data items;   (f) repeating steps (a) through (e) for one or more second users;   (g) comparing ratings provided by said first user and one or more second users for said plurality of individual data items to identify correlations between such users and/or items;   (h) generating a prediction and/or a recommendation for the first user concerning at least a third data item based in part on step (g).   
     
     
         15 . The method of  claim 14  wherein said data items are human perceivable media items. 
     
     
         16 . The method of  claim 15  wherein said data items are movies. 
     
     
         17 . A method of generating automatic recommendations for content to a first user with a computing system comprising:
 (a) providing a first database correlating a plurality of individual data items and rankings for a first user, wherein at least some of said individual data items represent human individuals;   (b) providing a second database correlating a plurality of topics or concepts to one or more of said plurality of individual data items;   (c) identifying first content presented to the first user with the computing system;   (d) analyzing said first content to identify one or more of said plurality of topics or concepts and any corresponding individual data item;   (e) identifying a rating provided by said first user with the computing system for said first content;   (f) generating a ranking for said corresponding individual data item from said rating for said first content;   (g) comparing rankings provided by said first user and one or more second users for said plurality of individual data items to identify correlations between such users and/or items;   (h) generating a prediction and/or a recommendation for the first user concerning a data item based in part on step (g).   
     
     
         18 . The method of  claim 17  wherein step (d) is performed by a natural language engine classifier. 
     
     
         19 . The method of  claim 18  further including a step: training said natural language engine with a training corpus. 
     
     
         20 . The method of  claim 17  where said first content includes at least one of: a) an advertisement presented to the first user; b) a search result list; c) human readable content reviewed on the Internet. 
     
     
         21 . The method of  claim 17  further including a step: customizing a search engine result by the first user concerning one of said topics or concepts based on said prediction and/or recommendation. 
     
     
         22 . The method of  claim 1  further including a step: presenting an advertisement along with said recommendation, which advertisement is based on a content of said recommendation. 
     
     
         23 . A method of generating automatic recommendations for content to a first user with a computing system comprising:
 (a) processing a set of first ratings from the first user for a first data source with the computing system, which first data source includes at least one of a human author, a social network contact, a message board, an RSS feed and/or a web log;   wherein said set of first ratings are weighted by at least one of the   following factors: 1) time; and/or 2) frequency;   (b) processing a set of second ratings from one or more second users for said first data source and one or more second data sources with the computing system, which second data sources also include at least one of a human author, a social network contact, a message board, an RSS feed and/or a web log;
 wherein said set of second ratings are also weighted by at least one of the following factors: 1) time; and/or 2) frequency; 
   (c) correlating said set of first ratings and said set of second ratings with the computing system to generate groups of users and/or groups of data sources suitable for a recommender system;   (d) generating a recommendation with the recommender system to said first user for one of said second data sources based on step (c).   
     
     
         24 . The method of  claim 23  further including a step: customizing a search engine result by the first user based on said prediction and/or recommendation. 
     
     
         25 . The method of  claim 23  further including a step: presenting an advertisement along with said recommendation, which advertisement is based on a content of said recommendation. 
     
     
         26 . A method of presenting advertising content in connection with an automatic recommendation to a user comprising:
 (a) identifying content presented to a plurality of users;   (b) processing said content with a natural language engine to classify and map such content to one or more topics;   (c) correlating a set of ad items to said one or more topics;   (d) causing a recommender system to generate a prediction and/or a recommendation for a user, said recommendation being related to one or more of said topics;   (e) presenting one of said set of ad items to the user as part of said prediction and/or recommendation.   
     
     
         27 . The method of  claim 26  further including a step: generating implicit ratings for said content based on behavior of said plurality of users. 
     
     
         28 . The method of  claim 27  further including a step: weighting said implicit ratings based on a time and/or a frequency of such ratings. 
     
     
         29 . The method of  claim 26  wherein said recommendation is related to a data source, including one of a human author, a social network contact, a message board, an RSS feed and/or a web log; 
     
     
         30 . A method of generating automatic recommendations to a first user in connection with an online message board with a computing system comprising:
 (a) identifying a first set of electronic messages on the online message board reviewed by the first user with the computing system;   (b) identifying a second set of electronic messages on the online message board reviewed by a plurality of second users with the computing system;   (c) evaluating a first set of ratings provided by the first user in connection with said first set of electronic messages and a second set of ratings provided by said plurality of second users for said second set of messages with the computing system;
 wherein said first set of ratings and said second set of ratings can be generated by at least one of an explicit rating and/or an implicit rating, which implicit rating is derived from online actions taken by said first user and said plurality of second users; 
   (d) generating a prediction and/or a recommendation for the first user from said first set of ratings and said second set of ratings which identifies at least one of: 1) one or more of said plurality of second users which are likely to be of interest to the first user; 2) one or more electronic messages which are likely to be of interest to the first user; 3) one or more electronic message authors which are likely to be of interest to the first user.   
     
     
         31 . The method of  claim 30  further including a step: customizing a search engine result by the first user based on said prediction and/or recommendation. 
     
     
         32 . The method of  claim 30  further including a step: presenting an advertisement along with said recommendation, which advertisement is based on a content of said recommendation. 
     
     
         33 . The method of  claim 30  further including a step: presenting an advertisement to said first user while he/she is reviewing an electronic message, which advertisement is based both on said first set of ratings as well as content of said electronic message. 
     
     
         34 . The method of  claim 30  wherein at least one of said explicit ratings and/or implicit ratings are given a weighting in accordance with a time characteristic. 
     
     
         35 . The method of  claim 34  wherein weighting increases for older ratings. 
     
     
         36 . The method of  claim 35  wherein said weighting is also adjusted based on a frequency of ratings provided for a particular data item. 
     
     
         37 . The method of  claim 30  wherein additional content reviewed by said first user and said plurality of second users at a separate website from said online message board as well as corresponding ratings for such content are also evaluated in determining said recommendation. 
     
     
         38 . The method of  claim 30  wherein said ratings are associated with at least one of: a user recommendation for an electronic message; a user designation of a preferred author for electronic messages; a user designation of an ignored author for electronic messages; a user recommendation for a particular topic; a user time spent reviewing an electronic message; a user search for a set of electronic messages; an ad selected by a user while reviewing an electronic message; a number of instances which a user has reviewed a selected electronic message. 
     
     
         39 . The method of  claim 30  including a step: identifying and publishing lists of groups of users with common ratings behavior. 
     
     
         40 . The method of  claim 30  including a step: identifying individual groups of users with common ratings behavior and providing suggestions to such groups for new memberships.