Bayesian latent variable models for collaborative item rating prediction

Harvey, Morgan, Carman, Mark J., Ruthven, Ian and Crestani, Fabio (2011) Bayesian latent variable models for collaborative item rating prediction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, pp. 699-708. ISBN 978-1-4503-0717-8

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Abstract

Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute 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 also each item's latent topics. We describe a Gibbs sampling procedure that can be 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, enhancing it with user-dependent and item-dependant biases to significantly improve rating estimation. We show by experiment on 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 other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better on the common and important case where the user profile is short.

Item Type:	Book Section
Subjects:	G400 Computer Science G500 Information Systems
Department:	Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User:	Morgan Harvey
Date Deposited:	27 Apr 2015 14:40
Last Modified:	10 Oct 2019 23:01
URI:	http://nrl.northumbria.ac.uk/id/eprint/22228

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