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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 orcid.org/0000-0001-6531-8907
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085679

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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