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Preference networks: Probabilistic models for recommendation systems

Truyen, TT, Phung, Quoc-Dinh and Venkatesh, S 2007, Preference networks: Probabilistic models for recommendation systems, Conferences in Research and Practice in Information Technology Series, vol. 70, pp. 195-202.

Title Preference networks: Probabilistic models for recommendation systems
Author(s) Truyen, TTORCID iD for Truyen, TT
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh
Venkatesh, S
Journal name Conferences in Research and Practice in Information Technology Series
Volume number 70
Start page 195
End page 202
Total pages 8
Publisher Australian Computer Society
Place of publication Sydney, N.S.W.
Publication date 2007-12-01
ISSN 1445-1336
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. © 2007, Australian Computer Society, Inc.
Language eng
Field of Research 0 Not Applicable
Socio Economic Objective 0 Not Applicable
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