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My researches focus on recommender systems specifically based on user-based collaborative filtering. The general assumption in collaborative filtering is that people which share similar ratings for common rated items will continue to do so in the future. Hence, the retrieval of like-minded individuals is crucial. We investigate a novel approach of retrieving like-minded individuals based on hypothesized preferences, which we hypothesize by means of machine learning, and semantic similarity of these hypothesized preferences applying Semantic Web technologies.
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