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Non-linear Matrix Factorization with Gaussian Processes
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BibTeX
@MISC{Lawrence_non-linearmatrix,
author = {Neil D. Lawrence and Raquel Urtasun},
title = {Non-linear Matrix Factorization with Gaussian Processes},
year = {}
}
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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-theart performance. 1.
Keyphrases
gaussian process non-linear matrix factorization non-linear probabilistic matrix factorization gaussian process latent variable model approximate method stochastic gradient descent popular approach matrix factorization previous state-of-theart performance data set movie recommender data set
