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One of the most efficient methods in collaborative filtering is matrixfactorization, which finds the latent vector representations of users and itemsbased on the ratings of users to items. However, a matrix factorization basedalgorithm suffers from the cold-start problem: it cannot find latent vectorsfor items to which previous ratings are not available. This paper utilizesclick data, which can be collected in abundance, to address the cold-startproblem. We propose a probabilistic item embedding model that learns itemrepresentations from click data, and a model named EMB-MF, that connects itwith a probabilistic matrix factorization for rating prediction. Theexperiments on three real-world datasets demonstrate that the proposed model isnot only effective in recommending items with no previous ratings, but alsooutperforms competing methods, especially when the data is very sparse.

A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data