Non-linear matrix factorization with Gaussian processes


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

Extracted Key Phrases

Citations per Year

166 Citations

Semantic Scholar estimates that this publication has received between 125 and 225 citations based on the available data.

See our FAQ for additional information.