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Preference networks : probabilistic models for recommendation systems

Truyen, Tran The, Phung, Dinh Q. and Venkatesh, Svetha 2007, Preference networks : probabilistic models for recommendation systems, in Data Mining and Analytics 2007 : Proceedings of the Sixth Australasian Data Mining Conference - AusDM 2007, Australian Computer Society, Sydney, N. S. W., pp. 191-198.

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Title Preference networks : probabilistic models for recommendation systems
Author(s) Truyen, Tran TheORCID iD for Truyen, Tran The orcid.org/0000-0001-6531-8907
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Venkatesh, Svetha
Conference name Australasian Data Mining Conference (6th : 2007 : Gold Coast, Qld.)
Conference location Gold Coast, Qld.
Conference dates 3-4 Dec. 2007
Title of proceedings Data Mining and Analytics 2007 : Proceedings of the Sixth Australasian Data Mining Conference - AusDM 2007
Editor(s) Christen, Peter
Kennedy, Paul
Li, Jiuyong
Kolyshkina, Inna
Williams, Graham
Publication date 2007
Conference series Australasian Data Mining Conference
Start page 191
End page 198
Total pages 8
Publisher Australian Computer Society
Place of publication Sydney, N. S. W.
Keyword(s) hybrid recommender systems
collaborative filtering
preference networks
conditional Markov networks
movie rating
Summary 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.
ISBN 9781920682514
ISSN 1445-1336
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044800

Document type: Conference Paper
Collections: School of Information Technology
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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.