Collaborative filtering via sparse Markov random fields

Truyen Tran, Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method.

Original languageEnglish
Pages (from-to)221-237
Number of pages17
JournalInformation Sciences
Volume369
DOIs
Publication statusPublished - 10 Nov 2016
Externally publishedYes

Keywords

  • Collaborative filtering
  • Dating recommendation
  • Markov random field
  • Movie recommendation
  • Recommender systems
  • Sparse graph learning

Cite this