Greenberg Traurig, LLP
Topic-oriented diversified item recommendation
A content recommendation system and method are provided in which content semantic topic analysis, user interest identification and per interest recommendations are used to deliver relevant and diversified content recommendations to the user. Semantic topic analysis is used to infer underlying topics in content items; for each content item, a topic distribution vector is derived with components that represent relevance of the content item to specific underlying topics. A user's long term and short term user interests are identified using the user's browsing history. Long term user interest(s) can be obtained by a weighted aggregation of topic distribution vectors of content items the user accessed. Short term interest can be represented by the topic distribution vector corresponding to a current content item. Using identified user's interests, relevant content items are selected for recommendations for the user.
Yahoo! Inc.
G
G06F
inventional
Wei Xianyu Peng
Li Zhao Liu