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US 2008/0077574 A1Published application
Topic Based Recommender System & Methods
OwnerJohn Nicholas Grossinventor
Life of this patent
Estimating the term…
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.
Claims40
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.
7 more elements
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.
1 more element
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).
5 more elements
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).
5 more elements
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).
8 more elements
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.
2 more elements
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.
5 more elements
Dependencies are inferred from the claim text.