Non-Linear Matrix Factorization with Gaussian Processes

Neil D. Lawrence; Raquel Urtasun

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Non-Linear Matrix Factorization with Gaussian Processes

Neil D. Lawrence, Raquel Urtasun

Proceedings of the International Conference in Machine Learning, Morgan Kauffman 26, 2009.

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.

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BibTeX


@InProceedings{Lawrence-nlmf09,
  title = 	 {Non-Linear Matrix Factorization with {G}aussian Processes},
  author = 	 {Lawrence, Neil D. and Urtasun, Raquel},
  booktitle = 	 {Proceedings of the International Conference in Machine Learning},
  year = 	 {2009},
  editor = 	 {Bottou, Leon and Littman, Michael},
  volume = 	 {26},
  address = 	 {San Francisco, CA},
  publisher =    {Morgan Kauffman},
  pdf = 	 {http://inverseprobability.com/publications/files/collab.pdf},
  url = 	 {/publications/lawrence-nlmf09.html},
  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.}
}

Endnote

%0 Conference Paper
%T Non-Linear Matrix Factorization with Gaussian Processes
%A Neil D. Lawrence
%A Raquel Urtasun
%B Proceedings of the International Conference in Machine Learning
%D 2009
%E Leon Bottou
%E Michael Littman	
%F Lawrence-nlmf09
%I Morgan Kauffman
%U /publications/lawrence-nlmf09.html
%V 26
%X 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.

RIS


TY  - CPAPER
TI  - Non-Linear Matrix Factorization with Gaussian Processes
AU  - Neil D. Lawrence
AU  - Raquel Urtasun
BT  - Proceedings of the International Conference in Machine Learning
DA  - 2009/06/01
ED  - Leon Bottou
ED  - Michael Littman	
ID  - Lawrence-nlmf09
PB  - Morgan Kauffman
VL  - 26
L1  - http://inverseprobability.com/publications/files/collab.pdf
UR  - /publications/lawrence-nlmf09.html
AB  - 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.
ER  -

APA


Lawrence, N.D. & Urtasun, R.. (2009). Non-Linear Matrix Factorization with Gaussian Processes. Proceedings of the International Conference in Machine Learning 26 Available from /publications/lawrence-nlmf09.html.