Two-Way Latent Grouping Model for User Preference Prediction

Reference:

Eerika Savia, Kai Puolamäki, Janne Sinkkonen, and Samuel Kaski. Two-way latent grouping model for user preference prediction. In Fahiem Bachus and Tommi Jaakkola, editors, Proceedings of UAI 2005, Uncertainty in Artificial Intelligence, pages 518–525, Corvallis, OH, 2005. AUAI Press.

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.

Suggested BibTeX entry:

@inproceedings{Savia05uai,
    address = {Corvallis, OH},
    author = {Eerika Savia and Kai Puolam{\"a}ki and Janne Sinkkonen and Samuel Kaski},
    booktitle = {Proceedings of UAI 2005, Uncertainty in Artificial Intelligence},
    editor = {Fahiem Bachus and Tommi Jaakkola},
    pages = {518--525},
    publisher = {AUAI Press},
    title = {Two-Way Latent Grouping Model for User Preference Prediction},
    year = {2005},
}