%0 Conference Paper
%A Stern, David
%A Herbrich, Ralf
%A Graepel, Thore
%B 18th International World Wide Web Conference
%C Madrid, Spain
%D 2009
%F www2009:12
%P 111-111
%T Matchbox: Large Scale Online Bayesian Recommendations
%U http://www2009.eprints.org/12/
%X 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 col-  laborative 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 user-  specific scale. Efficient inference is achieved by approxi-  mate message passing involving a combination of Expecta-  tion Propagation (EP) and Variational Message Passing. We  also include a dynamics model which allows an item’s popu-  larity, a user’s taste or a user’s personal rating scale to drift  over time. By using Assumed-Density Filtering (ADF) for  training, the model requires only a single pass through the  training data. This is an on-line learning algorithm capable  of incrementally taking account of new data so the system  can immediately reflect the latest user preferences. We eval-  uate the performance of the algorithm on the MovieLens and  Netflix data sets consisting of approximately 1,000,000 and  100,000,000 ratings respectively. This demonstrates that  training the model using the on-line ADF approach yields  state-of-the-art performance with the option of improving  performance further if computational resources are available  by performing multiple EP passes over the training data.