Non-linear matrix factorization with Gaussian processes

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.

DOI: 10.1145/1553374.1553452
View Slides

Extracted Key Phrases

010203020102011201220132014201520162017
Citations per Year

182 Citations

Semantic Scholar estimates that this publication has 182 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Lawrence2009NonlinearMF, title={Non-linear matrix factorization with Gaussian processes}, author={Neil D. Lawrence and Raquel Urtasun}, booktitle={ICML}, year={2009} }