Matchbox: large scale online bayesian recommendations

We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional `trait space' in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences. Here we present three alternatives: direct observation of an absolute rating each user gives to some items, observation of a binary preference (like/ don't like) and observation of a set of ordinal ratings on a userspecific scale. Efficient inference is achieved by approximate message passing involving a combination of Expectation Propagation (EP) and Variational Message Passing. ...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: David H. Stern, Ralf Herbrich, Thore Graepel
• Keywords: ADF Approach Yields, Internet Technology, Item Meta Data, Latest User Preferences, WWW 2009