"Bayesian Latent Variable Models for Collaborative Item Rating Prediction." M. Harvey, I. Ruthven, F. Crestani, and M. Carman. ACM 20th Conference on Information and Knowledge Management, CIKM. October 2011.
Abstract: Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such it is an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and each item's latent topics. We describe the Gibbs sampling procedure used to estimate its parameters and show by experiment that it is competitive with the gradient descent SVD methods commonly used in state-of-the-art systems. We then proceed to make an important and novel extension to this model, allowing it to include user and item-dependant biases to significantly improve rating estimation.
We show by experiment with a large set of real ratings data that these models are able to outperform 3 common baselines, including a very competitive and modern SVD-based model. Furthermore we illustrate a number of other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better in the common and very important case where the user profile is short.