phung-preferencenetworks-2007.pdf (380.89 kB)
Preference Networks: probabilistic models for recommendation systems
conference contribution
posted on 2007-12-03, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshAbstract
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.
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.
History
Event
Australasian Data Mining Conference. (6th : 2007 : Gold Coast, N.S.W.)Pagination
195 - 202Publisher
Australian Computer SocietyLocation
Gold Coast, N.S.W.Place of publication
Gold Coast, N.S.W.Start date
2007-12-03End date
2007-12-04Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2007, Australian Computer SocietyEditor/Contributor(s)
P Christen, P Kennedy, J Li, I Kolyshkina, G WilliamsTitle of proceedings
AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference.Usage metrics
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