Interaction and personalization of criteria in recommender systems

Authors:
Shawn R. Wolfe;Yi Zhang
Affiliations:
School of Engineering, University of California Santa Cruz, Santa Cruz, CA;School of Engineering, University of California Santa Cruz, Santa Cruz, CA
Venue:
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Year:
2010

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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.