Ordinal Boltzmann machines for collaborative filtering

Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh

Research output: Contribution to conferencePaperpeer-review

48 Citations (Scopus)


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 method.

Original languageEnglish
Number of pages9
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventConference in Uncertainty in Artificial Intelligence 2009 - Montreal, Canada
Duration: 18 Jun 200921 Jun 2009
Conference number: 25th
https://dl.acm.org/doi/proceedings/10.5555/1795114 (Proceedings)


ConferenceConference in Uncertainty in Artificial Intelligence 2009
Abbreviated titleUAI 2009
Internet address

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