PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Latent grouping models for user preference prediction
Eerika Savia, Kai Puolamäki and Samuel Kaski
Machine Learning Volume 74, Number 1, pp. 75-109, 2008. ISSN 1573-0565


We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves predictions in the case of new users. We show that if either users and documents or both are new, two-way generalization becomes necessary. We demonstrate the benefits of grouping of users, grouping of documents, and two-way grouping, with artificial data and in two case studies with real data. We have introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare 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 Two-Way Model predicts relevance more accurately when the target consists of both new documents and new users. The reason is that generalization over documents becomes beneficial for new documents and at the same time generalization over users is needed for new users.

EPrint Type:Article
Additional Information:Published electronically in 09/2008. Print version appeared in 01/2009.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:4260
Deposited By:Eerika Savia
Deposited On:24 January 2009