Two-Way Latent Grouping Model for User Preference Prediction
Eerika Savia, Kai Puolamaki, Janne Sinkkonen, Samuel Kaski
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.
PDF Link: /papers/05/p518-savia.pdf
AUTHOR = "Eerika Savia
and Kai Puolamaki and Janne Sinkkonen and Samuel Kaski",
TITLE = "Two-Way Latent Grouping Model for User Preference Prediction",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "518--524"