PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Two-way latent grouping model for user preference prediction
Eerika Savia, Kai Puolamäki, Janne Sinkkonen and Samuel Kaski
In: UAI 2005, 26-29 July 2005, Edinburgh, Scotland.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1005
Deposited By:Eerika Savia
Deposited On:06 July 2005