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Published in:
2011 | OriginalPaper | Chapter
Experiments in Bayesian Recommendation
Authors : Thomas Barnard, Adam Prügel-Bennett
Published in: Advances in Intelligent Web Mastering – 3
Publisher: Springer Berlin Heidelberg
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The performance of collaborative filtering recommender systems can suffer when data is sparse, for example in distributed situations. In addition popular algorithms such as memory-based collaborative filtering are rather ad-hoc, making principled improvements difficult. In this paper we focus on a simple recommender based on naïve Bayesian techniques, and explore two different methods of modelling probabilities.We find that a Gaussian model for rating behaviour works well, and with the addition of a Gaussian-Gamma prior it maintains good performance even when data is sparse.