Preference Networks: probabilistic models for recommendation systems Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2007, Preference Networks: probabilistic models for recommendation systems, in AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference., Australian Computer Society, Gold Coast, N.S.W., pp. 195-202. Attached Files Name Description MIMEType Size Downloads tran-preferencenetworks-2007.pdf Published version application/pdf 439.12KB 36 Title Preference Networks: probabilistic models for recommendation systems Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Australasian Data Mining Conference. (6th : 2007 : Gold Coast, N.S.W.) Conference location Gold Coast, N.S.W. Conference dates 2007/12/3 - 2007/12/4 Title of proceedings AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference. Editor(s) Christen, Peter Kennedy, Paul Li, Jiuyong Kolyshkina, Inna Williams, Graham Publication date 2007 Start page 195 End page 202 Total pages 8 Publisher Australian Computer Society Place of publication Gold Coast, N.S.W. Keyword(s) Hybrid RecommenderSystems Collaborative Filtering Preference Networks Conditional Markov Networks Movie Rating Summary Abstract Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN. Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2007, Australian Computer Society Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30072930 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection Related Links Link Description Connect to Elements publication management system 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 04 May 2015, 10:46:13 EST Mon, 04 May 2015, 10:46:56 EST Tue, 05 May 2015, 05:01:50 EST Tue, 05 May 2015, 06:08:33 EST Thu, 04 Jun 2015, 15:53:39 EST Thu, 04 Jun 2015, 15:54:29 EST Thu, 04 Jun 2015, 16:05:42 EST Thu, 03 Mar 2016, 14:11:53 EST Thu, 03 Mar 2016, 14:14:55 EST Thu, 03 Mar 2016, 14:16:55 EST Wed, 07 Sep 2016, 09:10:24 EST Wed, 06 Dec 2017, 06:14:36 EST Fri, 07 Sep 2018, 17:30:58 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 236 Abstract Views, 38 File Downloads  -  Detailed Statistics Created: Mon, 04 May 2015, 10:46:56 EST