Preference networks: Probabilistic models for recommendation systems

Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationData Mining and Analytics 2007 - 6th Australasian Data Mining Conference, AusDM 2007, Proceedings
Pages195-202
Number of pages8
Publication statusPublished - 1 Dec 2007
Externally publishedYes
EventAustralasian Data Mining Conference 2007 - Gold Coast, Australia
Duration: 3 Dec 20074 Dec 2007
Conference number: 6th
https://dblp.org/db/conf/ausdm/ausdm2007.html (Proceedings)

Publication series

NameConferences in Research and Practice in Information Technology Series
Volume70
ISSN (Print)1445-1336

Conference

ConferenceAustralasian Data Mining Conference 2007
Abbreviated titleAusDM 2007
CountryAustralia
CityGold Coast
Period3/12/074/12/07
Internet address

Keywords

  • Collaborative filtering
  • Conditional markov networks
  • Hybrid recommender systems
  • Movie rating
  • Preference networks

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