Xiaotong Shen (University of Minnesota)

Event Date: 

Wednesday, November 9, 2016 -
3:30pm to 5:00pm

Event Date Details: 

Refreshments served at 3:15pm

Event Location: 

  • Sobel Seminar Room; South Hall 5607F
  • Department Seminar Series

Abstract: 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 discuss various aspects of latent factor modeling, in addition to computational strategies for large problems.