Abstract
AbstractThe PhD research presented in this paper addresses some of the problems involved in creating a context-aware personalized service. Our main interest is in the steps of defining, detecting, acquiring and using real and relevant context of users. Our goals are to: collect and publish a context-rich movie recommender database, add theoretical requirements for contextual information in existing definitions of context, develop a methodology for relevant-context detection and inspect the impact of relevant and irrelevant context on the rating prediction using the matrix-factorization algorithm. This paper presents the work done so far and future plans with open issues.Keywordsuser modelingrecommender systemscontextual information
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