A non-IID framework for collaborative filtering with Restricted Boltzmann Machines

Kostadin Georgiev, Preslav Nakov

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

53 Citations (Scopus)

Abstract

We propose a framework for collaborative filtering based on Restricted Boltzmann Machines (RBM), which extends previous RBM-based approaches in several important directions. First, while previous RBM research has focused on modeling the correlation between item ratings, we model both user-user and item-item correlations in a unified hybrid non-IID framework. We further use real values in the visible layer as opposed to multinomial variables, thus taking advantage of the natural order between user-item ratings. Finally, we explore the potential of combining the original training data with data generated by the RBM-based model itself in a bootstrapping fashion. The evaluation on two MovieLens datasets (with 100K and 1M user-item ratings, respectively), shows that our RBM model rivals the best previously-proposed approaches.

Original languageEnglish
Title of host publication30th International Conference on Machine Learning, ICML 2013
PublisherInternational Machine Learning Society (IMLS)
Pages2185-2193
Number of pages9
EditionPART 3
Publication statusPublished - 1 Jan 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: 16 Jun 201321 Jun 2013

Other

Other30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period16/6/1321/6/13

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

Cite this

Georgiev, K., & Nakov, P. (2013). A non-IID framework for collaborative filtering with Restricted Boltzmann Machines. In 30th International Conference on Machine Learning, ICML 2013 (PART 3 ed., pp. 2185-2193). International Machine Learning Society (IMLS).