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Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09

DOI: 10.1145/1553374.1553452

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Non-linear matrix factorization with Gaussian processes

Proceedings article published in 2009 by Neil D. Lawrence ORCID, Raquel Urtasun
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This paper is available in a repository.

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Abstract

A popular approach to collaborative filtering is matrix factorization. In this paper we develop a non-linear probabilistic matrix factorization using Gaussian process latent variable models. We use stochastic gradient descent (SGD) to optimize the model. SGD allows us to apply Gaussian processes to data sets with millions of observations without approximate methods. We apply our approach to benchmark movie recommender data sets. The results show better than previous state-of-the-art performance.