You are not logged in.

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 orcid.org/0000-0001-6531-8907
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30096290

Document type: Journal Article
Collections: Provisional Records Queue
Unverified Records Temporary Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 7 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 6 Abstract Views  -  Detailed Statistics
Created: Tue, 16 May 2017, 15:04:55 EST

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.