Collaborative Learning of Preferences for Recommending Games and Media
published: Jan. 24, 2012, recorded: December 2011, views: 4408
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The talk is motivated by our recent work on a recommender system for games, videos, and music on Microsoft’s Xbox Live Marketplace with over 35M users. I will discuss the challenges associated with such a task including the type of data available, the nature of the user feedback data, implicit versus explicit, and the scale of the problem. I will then describe a probabilistic graphical model that combines the prediction of pairwise and listwise preferences with ideas from matrix factorisation and contentbased recommender systems to meet some of these challenges. The new model combines ideas from two other models, TrueSkill and Matchbox, which will be reviewed. TrueSkill is a model for estimating players’ skills based on outcome rankings in online games on Xbox Live, and Matchbox is a Bayesian recommender system based on mapping user/item features into a common trait space. This is joint work with Tim Salimans and Ulrich Paquet. Contributors to TrueSkill include Ralf Herbrich and Tom Minka, contributors to Matchbox include Ralf Herbrich and David Stern.
Download slides: nipsworkshops2011_graepel_collaborative_01.pdf (4.4 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !