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. Attached Files Name Description MIMEType Size Downloads Title Collaborative filtering via sparse Markov random fields Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 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 Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 25 Aug 2016, 16:59:20 EST Thu, 25 Aug 2016, 17:00:04 EST Fri, 26 Aug 2016, 05:00:52 EST Thu, 29 Sep 2016, 08:50:37 EST Fri, 30 Sep 2016, 06:09:28 EST Mon, 17 Oct 2016, 12:31:44 EST Mon, 17 Oct 2016, 12:34:54 EST Mon, 17 Oct 2016, 12:35:45 EST Fri, 31 Mar 2017, 10:51:16 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 99 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 29 Sep 2016, 08:50:37 EST

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