Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/1390156.1390267acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicmlConference Proceedingsconference-collections
research-article

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Published: 05 July 2008 Publication History

Abstract

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets. However, unless the regularization parameters are tuned carefully, this approach is prone to overfitting because it finds a single point estimate of the parameters. In this paper we present a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters. We show that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million movie ratings. The resulting models achieve significantly higher prediction accuracy than PMF models trained using MAP estimation.

References

[1]
Hinton, G. E., & van Camp, D. (1993). Keeping the neural networks simple by minimizing the description length of the weights. COLT (pp. 5--13).
[2]
Hofmann, T. (1999). Probabilistic latent semantic analysis. Proceedings of the 15th Conference on Uncertainty in AI (pp. 289--296). San Fransisco, California: Morgan Kaufmann.
[3]
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37, 183.
[4]
Lim, Y. J., & Teh, Y. W. (2007). Variational Bayesian approach to movie rating prediction. Proceedings of KDD Cup and Workshop.
[5]
Marlin, B. (2004). Modeling user rating profiles for collaborative filtering. In S. Thrun, L. Saul and B. Schölkopf (Eds.), Advances in neural information processing systems 16. Cambridge, MA: MIT Press.
[6]
Marlin, B., & Zemel, R. S. (2004). The multiple multiplicative factor model for collaborative filtering. Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada. ACM.
[7]
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Technical Report CRG-TR-93-1). Department of Computer Science, University of Toronto.
[8]
Nowlan, S. J., & Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing. Neural Computation, 4, 473--493.
[9]
Raiko, T., Ilin, A., & Karhunen, J. (2007). Principal component analysis for large scale problems with lots of missing values. ECML (pp. 691--698).
[10]
Rennie, J. D. M., & Srebro, N. (2005). Fast maximum margin matrix factorization for collaborative prediction. Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany (pp. 713--719). ACM.
[11]
Salakhutdinov, R., & Mnih, A. (2008). Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
[12]
Srebro, N., & Jaakkola, T. (2003). Weighted low-rank approximations. Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), Washington, DC, USA (pp. 720--727). AAAI Press.

Cited By

View all
  • (2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
  • (2024)Movie recommendation and classification system using block chainWeb Intelligence10.3233/WEB-23034622:4(659-680)Online publication date: 15-Nov-2024
  • (2024)Leveraging user’s preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rateJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23126447:1-2(1-13)Online publication date: 18-Nov-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICML '08: Proceedings of the 25th international conference on Machine learning
July 2008
1310 pages
ISBN:9781605582054
DOI:10.1145/1390156
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Pascal
  • University of Helsinki
  • Xerox
  • Federation of Finnish Learned Societies
  • Google Inc.
  • NSF
  • Machine Learning Journal/Springer
  • Microsoft Research: Microsoft Research
  • Intel: Intel
  • Yahoo!
  • Helsinki Institute for Information Technology
  • IBM: IBM

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2008

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

ICML '08
Sponsor:
  • Microsoft Research
  • Intel
  • IBM

Acceptance Rates

Overall Acceptance Rate 140 of 548 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)181
  • Downloads (Last 6 weeks)17
Reflects downloads up to 14 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
  • (2024)Movie recommendation and classification system using block chainWeb Intelligence10.3233/WEB-23034622:4(659-680)Online publication date: 15-Nov-2024
  • (2024)Leveraging user’s preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rateJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23126447:1-2(1-13)Online publication date: 18-Nov-2024
  • (2024)SNCA: Semi-Supervised Node Classification for Evolving Large Attributed GraphsBig Data Mining and Analytics10.26599/BDMA.2024.90200337:3(794-808)Online publication date: Sep-2024
  • (2024)LSTGCN: Inductive Spatial Temporal Imputation Using Long Short-Term DependenciesACM Transactions on Knowledge Discovery from Data10.1145/369064518:9(1-25)Online publication date: 8-Nov-2024
  • (2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
  • (2024)Building Trust in Decision with Conformalized Multi-view Deep ClassificationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681297(7278-7287)Online publication date: 28-Oct-2024
  • (2024)Enhancing E-commerce Recommender Systems through Multi-Objective Immune AlgorithmProceedings of the 7th International Conference on Networking, Intelligent Systems and Security10.1145/3659677.3659702(1-1)Online publication date: 18-Apr-2024
  • (2024)User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645331(334-343)Online publication date: 13-May-2024
  • (2024)Bayesian Dictionary Learning on Robust Tubal Transformed Tensor FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.324815635:8(11091-11105)Online publication date: Aug-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media