Non-Linear Matrix Factorization with Gaussian Processes

author: Raquel Urtasun, Department of Computer Science, University of Toronto
published: Aug. 26, 2009,   recorded: June 2009,   views: 10841

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A popular approach to collaborative filtering is matrix factorization. In this paper we consider the "probabilistic matrix factorization" and by taking a latent variable model perspective we show its equivalence to Bayesian PCA. This inspires us to consider probabilistic PCA and its non-linear extension, the Gaussian process latent variable model (GP-LVM) as an approach for probabilistic non-linear matrix factorization. We apply approach to benchmark movie recommender data sets. The results show better than previous state-of-the-art performance.

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