Generalized probabilistic matrix factorizations for collaborative filtering

Hanhuai Shan, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

118 Scopus citations

Abstract

Probabilistic matrix factorization (PMF) methods have shown great promise in collaborative filtering. In this paper, we consider several variants and generalizations of PMF framework inspired by three broad questions: Are the prior distributions used in existing PMF models suitable, or can one get better predictive performance with different priors? Are there suitable extensions to leverage side information? Are there benefits to taking into account row and column biases? We develop new families of PMF models to address these questions along with efficient approximate inference algorithms for learning and prediction. Through extensive experiments on movie recommendation datasets, we illustrate that simpler models directly capturing correlations among latent factors can outperform existing PMF models, side information can benefit prediction accuracy, and accounting for row/column biases leads to improvements in predictive performance.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages1025-1030
Number of pages6
DOIs
StatePublished - 2010
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining, ICDM 2010
Country/TerritoryAustralia
CitySydney, NSW
Period12/14/1012/17/10

Keywords

  • Probabilistic matrix factorization
  • Topic models
  • Variational inference

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