Interaction and Personalization of Criteria in Recommender Systems

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Interaction and Personalization of Criteria in Recommender Systems
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. Key words: information filtering, multiple criteria, nonlinear models
Shawn R. Wolfe, Yi Zhang
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2010
Where UM
Authors Shawn R. Wolfe, Yi Zhang
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