Modeling item selection and relevance for accurate recommendations: a bayesian approach
Proceedings of the fifth ACM conference on Recommender systems
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In this paper we extend the formulation of the User Rating Profile model, providing a Gibbs Sampling derivation for parameter estimation. Validation tests on Movielens data show that the proposed approach outperforms significantly the variational version in terms of both prediction accuracy and learning time. Gibbs Sampling provides a simple and flexible learning procedure which can be extended to include external evidence, in the form of soft constraints. More specifically, given a-priori information about user-neighbors, we propose an effective regularization technique that drives the first sampling iterations pushing the model towards a state which better represents the user-neighborhoods specified in input.