Bayesian nonparametric poisson factorization for recommendation systems

Prem Gopalan, Francisco J.R. Ruiz, Rajesh Ranganath, David M. Blei

Research output: Contribution to journalConference articlepeer-review


We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and effectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an efficient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with fixed dimensionality. We studied our model and algorithm with large real-world data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.

Original languageEnglish (US)
Pages (from-to)275-283
Number of pages9
JournalJournal of Machine Learning Research
StatePublished - 2014
Event17th International Conference on Artificial Intelligence and Statistics, AISTATS 2014 - Reykjavik, Iceland
Duration: Apr 22 2014Apr 25 2014

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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