Abstract
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.
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Barnard, T., Prügel-Bennett, A. (2011). Experiments in Bayesian Recommendation. In: Mugellini, E., Szczepaniak, P.S., Pettenati, M.C., Sokhn, M. (eds) Advances in Intelligent Web Mastering – 3. Advances in Intelligent and Soft Computing, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18029-3_5
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DOI: https://doi.org/10.1007/978-3-642-18029-3_5
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