A novel collective matrix factorization model for recommendation with fine-grained social trust prediction Wang, Jinkun, Zhang, Shasha, Liu, Xiao, Jiang, Yuanchun and Zhang, Min 2017, A novel collective matrix factorization model for recommendation with fine-grained social trust prediction, Concurrency computation, vol. 29, no. 19, pp. 1-14, doi: 10.1002/cpe.4233. Attached Files Name Description MIMEType Size Downloads Title A novel collective matrix factorization model for recommendation with fine-grained social trust prediction Author(s) Wang, Jinkun Zhang, Shasha Liu, Xiao orcid.org/0000-0001-8400-5754 Jiang, Yuanchun Zhang, Min Journal name Concurrency computation Volume number 29 Issue number 19 Article ID e4233 Start page 1 End page 14 Total pages 14 Publisher Wiley Place of publication Chichester, Eng. Publication date 2017-10-10 ISSN 1532-0626 1532-0634 Keyword(s) collective matrix factorization recommender systems trust and distrust weighted social trust matrix Science & Technology Technology Computer Science, Software Engineering Computer Science, Theory & Methods Computer Science SYSTEMS DISTRUST Language eng DOI 10.1002/cpe.4233 Field of Research 0805 Distributed Computing 0803 Computer Software HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2017, Wiley Persistent URL http://hdl.handle.net/10536/DRO/DU:30102654 Document type: Journal Article Collection: School of Information Technology Related Links Link Description 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 Tue, 19 Sep 2017, 11:41:31 EST Tue, 19 Sep 2017, 12:42:49 EST Wed, 20 Sep 2017, 08:17:19 EST Fri, 03 Nov 2017, 05:03:24 EST Thu, 09 Nov 2017, 10:07:31 EST Thu, 09 Nov 2017, 10:10:28 EST Thu, 09 Nov 2017, 10:11:06 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 167 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 19 Sep 2017, 11:41:31 EST

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