We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimate both models by Gibbs sampling. The new method predicts relevance more accurately for new documents that have few known ratings. The reason is that generalization over documents then becomes necessary and hence the two-way grouping is profitable.

This work was supported by the Academy of Finland, decision no. 79017, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778.