PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Two-way grouping by one-way topic models
Eerika Savia, Kai Puolamäki and Samuel Kaski
Advances in Intelligent Data Analysis VIII pp. 178-189, 2009.

Abstract

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. The same applies for documents in the case of new users. We have shown earlier that if there are both new users and new documents, two-way generalization becomes necessary, and introduced a probabilistic Two-Way Model for the task. The task of finding a two-way grouping is a non-trivial combinatorial problem, which makes it computationally difficult. We suggest approximating the Two-Way Model with two URP models; one that groups users and one that groups documents. Their two predictions are combined using a product of experts model. This combination of two one-way models achieves even better prediction performance than the original Two-Way Model.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6267
Deposited By:Kai Puolamäki
Deposited On:08 March 2010