Non-Linear Matrix Factorization with Gaussian Processes

Neil D. LawrenceRaquel 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.

Cite this Paper


BibTeX
@InProceedings{Lawrence:nlmf09, title = {Non-Linear Matrix Factorization with Gaussian Processes}, author = {Neil D. Lawrence and Raquel Urtasun}, booktitle = {Proceedings of the International Conference in Machine Learning}, year = {2009}, editor = {Leon Bottou and Michael Littman}, volume = {26}, address = {San Francisco, CA}, publisher = {Morgan Kauffman}, 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 %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 - 2009/01/01 ED - Leon Bottou ED - Michael Littman ID - Lawrence:nlmf09 PB - Morgan Kauffman VL - 26 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

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