Collaborative filtering with decoupled models for preferences and ratings

Rong Jin, Luo Si, Chengxiang Zhai, Jamie Callan

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.

Original languageEnglish (US)
Pages309-316
Number of pages8
StatePublished - Dec 1 2003
EventCIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management - New Orleans, LA, United States
Duration: Nov 3 2003Nov 8 2003

Other

OtherCIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management
Country/TerritoryUnited States
CityNew Orleans, LA
Period11/3/0311/8/03

Keywords

  • Collaborative filtering
  • Preference model
  • Probabilistic model
  • Rating model

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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