PrivacyGroup Event:2017/11/20 Talk on recommender systems

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Personalized Prediction and Recommender Systems

Xiaotong T. Shen (University of Minnesota <>) is presenting at the Statistics GIDP Colloquium

Monday, November 20, 2017

3:00 PM ENR2 S395


Personalized prediction predicts a user's preference for a large number of items through user-specific as well as content-specific information, based on a very small amount of observed preference scores. In a sense, predictive accuracy depends on how to pool the information from similar users and items. Two major approaches are collaborative filtering and content-based filtering. Whereas the former utilizes the information on users that think alike for a specific item, the latter acts on characteristics of the items that a user prefers, on which two kinds of recommender systems Grooveshark and Pandora are built. In this talk, I will review some recent advances in latent factor modeling and discuss various issues as well as scalable strategies based on a ""divide-and-conquer" algorithm.