Collaborative filtering via sparse Markov random fields

Tran, Truyen, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Collaborative filtering via sparse Markov random fields, Information sciences, vol. 369, pp. 221-237, doi: 10.1016/j.ins.2016.06.027.

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Title Collaborative filtering via sparse Markov random fields
Author(s) Tran, TruyenORCID iD for Tran, Truyen
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name Information sciences
Volume number 369
Start page 221
End page 237
Total pages 17
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-11-10
ISSN 0020-0255
Keyword(s) recommender systems
collaborative filtering
Markov random field
sparse graph learning
movie recommendation
dating recommendation
Summary 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.
Language eng
DOI 10.1016/j.ins.2016.06.027
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
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