Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models

Suhrid Balakrishnan, Sumit Chopra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While latent factor models are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning (or refining) user parameters from such feedback. We propose a scheme where users are presented with a sequence of pairwise preference questions: "Do you prefer item A over B?". User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporating the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain criterion. We validate the scheme on the Netflix movie ratings data set. A user study and automated experiments validate our findings.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages725-730
Number of pages6
DOIs
StatePublished - 2010
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference10th IEEE International Conference on Data Mining, ICDM 2010
Country/TerritoryAustralia
CitySydney, NSW
Period12/14/1012/17/10

Keywords

  • Active learning
  • Latent factor models
  • Pairwise preferences
  • Recommender systems

ASJC Scopus subject areas

  • Engineering(all)

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