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

• Type: Conference
• Year: 2010
• Where: UM
• Authors: Shawn R. Wolfe, Yi Zhang
• Keywords: Nonlinear Interaction, Programming Languages, UM 2010, User Model, User’s Informational Need