Personalized implicit learning in a music recommender system
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Information Sciences: an International Journal
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An amount of information on the Web has been increased explosively with the growth of information technology. In the area of electronic commerce, the recommender systems that provide personalized content are crucial research issue. The analysis of efficient user preference is important for improving the recommendation accuracy. Existing recommendation system has used implicit feedback for analyzing user preference. But, when the collected user information is lack, it is not fit. This paper proposes a personalized recommendation system which is based on information built by analyzing implicit feedback. The proposed system monitors the various user behaviors comprehensively to analyze user intention more precisely. The system also deduces the most important attribute for the user among various attribute of a product based on ID3 algorithm, and applies the result to analyzing user preference. Therefore, proposed system is able to recommend items in the situation that user behavior information is lack. Empirical results show that the effectiveness of the system is confirmed.