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Interaction and Personalization of Criteria in Recommender Systems

  • Shawn R. Wolfe
  • Yi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)

Abstract

A user’s informational need and preferences can be modeled by criteria, which in turn can be used to prioritize candidate results and produce a ranked list. We examine the use of such a criteria-based user model separately in two representative recommendation tasks: news article recommendations and product recommendations. We ask the following: are there nonlinear interactions among the criteria; and should the models be personalized? We assume that that user ratings on each criterion are available, and use machine learning to infer a user model that combines these multiple ratings into a single overall rating. We found that the ratings of different criteria have a nonlinear interaction in some cases, for example, article novelty and subject relevance often interact. We also found that these interactions vary from user to user.

Keywords

information filtering multiple criteria nonlinear models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shawn R. Wolfe
    • 1
    • 2
  • Yi Zhang
    • 1
  1. 1.School of EngineeringUniversity of California Santa CruzSanta CruzUSA
  2. 2.NASA Ames Research CenterMoffett FieldUSA

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