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Ordinal Boltzmann machines for collaborative filtering
conference contribution
posted on 2009-01-01, 00:00 authored by T Truyen, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshCollaborative 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.
History
Event
Uncertainty in Artificial Intelligence. Conference (25th : Montreal, Quebec)Pagination
548 - 556Publisher
AUAI PressLocation
Montreal, QuebecPlace of publication
Arlington, Va.Start date
2009-06-18End date
2009-06-21ISBN-13
9780974903958Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2009, AUAI PressTitle of proceedings
UAI 2009 : Proceedings of the 25th Conference on Uncertainty in Artificial IntelligenceUsage metrics
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