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Ordinal Boltzmann machines for collaborative filtering

Truyen, Tran The, Phung, Dinh Q. and Venkatesh, Svetha 2009, Ordinal Boltzmann machines for collaborative filtering, in UAI 2009 : Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, AUAI Press, Arlington, Va., pp. 548-556.

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Title Ordinal Boltzmann machines for collaborative filtering
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, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Uncertainty in Artificial Intelligence. Conference (25th : Montreal, Quebec)
Conference location Montreal, Quebec
Conference dates 18-21 Jun. 2009
Title of proceedings UAI 2009 : Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence
Editor(s) [Unknown]
Publication date 2009
Conference series Uncertainty in Artificial Intelligence. Conference
Start page 548
End page 556
Total pages 9
Publisher AUAI Press
Place of publication Arlington, Va.
Keyword(s) Boltzmann machines
co-occurrence
co-occurrence pattern
collaborative filtering
dimensionality reduction techniques
preference data
probabilistic models
recommendation techniques
Summary Collaborative filtering is an effective recommendation technique wherein the preference of an individual can potentially be predicted based on preferences of other members. Early algorithms often relied on the strong locality in the preference data, that is, it is enough to predict preference of a user on a particular item based on a small subset of other users with similar tastes or of other items with similar properties. More recently, dimensionality reduction techniques have proved to be equally competitive, and these are based on the co-occurrence patterns rather than locality. This paper explores and extends a probabilistic model known as Boltzmann Machine for collaborative filtering tasks. It seamlessly integrates both the similarity and cooccurrence in a principled manner. In particular, we study parameterisation options to deal with the ordinal nature of the preferences, and propose a joint modelling of both the user-based and item-based processes. Experiments on moderate and large-scale movie recommendation show that our framework rivals existing well-known methods.
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ISBN 9780974903958
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 ©2009, AUAI Press
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044560

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.