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How to overcome stumbling blocks of traditional personalization paradigms

Abstract:

N owadays, users are exposed to an overwhelming amount of information in several application domains. Recommender systems fight such an overload by selecting the products which are more appealing to each user, according to his personal preferences or needs. Current personalization techniques are based on more or less sophisticated syntactic methods which miss a lot of knowledge during the elaboration of the recommendations. In this paper, we propose an approach that effectively overcomes the drawbacks of the existing personalization techniques by resorting to reasoning mechanisms inspired in Semantic Web technologies. Such a reasoning provides recommender system with extra knowledge about the user's preferences, thus favoring more accurate personalization processes. © 2009 Springer US.