Dynamic poisson factorization

Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei

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

Abstract

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.

Original languageEnglish (US)
Title of host publicationRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages155-162
Number of pages8
ISBN (Electronic)9781450336925
DOIs
StatePublished - Sep 16 2015
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: Sep 16 2015Sep 20 2015

Publication series

NameRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Other

Other9th ACM Conference on Recommender Systems, RecSys 2015
CountryAustria
CityVienna
Period9/16/159/20/15

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Science Applications
  • Control and Systems Engineering

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  • Cite this

    Charlin, L., Ranganath, R., McInerney, J., & Blei, D. M. (2015). Dynamic poisson factorization. In RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems (pp. 155-162). (RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/2792838.2800174