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You are what you consume: a bayesian method for personalized recommendations

Evangelos Tripolitakis

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Summary

In this paper, we propose a novel Bayesian approach for personalized recommendations. In our approach, we model both user preferences and items under recommendation as multivariate Gaussian distributions; and make use of Normal-Inverse Wishart priors to model the recommendation agent beliefs about user types. We employ a lightweight agent-user interaction process, during which the user is presented with and asked to rate a small number of items. We then interpret these ratings in an innovative way, using them to guide a Bayesian updating process that helps us both capture a user's current mood, and maintain her overall user type. We produced several variants of our approach, and applied them in the movie recommendations domain, evaluating them on data from the MovieLens dataset. Our algorithms are shown to be competitive against a state-of-the-art method, which nevertheless requires a minimum set of ratings from various users to provide recommendations---unlike our entirely personalized approach.

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