Two-Way Latent Grouping Model for User Preference Prediction
This is an incremental improvement for recommendation systems, addressing generalization over documents with limited data.
The paper tackles the problem of predicting user preferences for new documents with few ratings by introducing a two-way latent grouping model for both users and documents, showing it predicts relevance more accurately than a state-of-the-art method that only groups users.
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 twoway grouping is profitable.