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Dynamic Recommender on User Taste Tendency Model : Focusing on Movie Recommender System  

이수정 (서울대학교 전기컴퓨터공학부)
이형동 (서울대학교 전기컴퓨터공학)
김형주 (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.2, 2004 , pp. 153-163 More about this Journal
Abstract
Many recommender systems are based on Content-based Filtering and Social Filtering Both methods have their own advantages and disadvantages, and they complement each other rather than compete. So incorporating of both methods can make the better system and combination technique controls the quality of the entire recommender system. In this paper, we presented each user has his own tendency to decide which is the better recommendation for himself among the various recommendation results, and suggested the Personalized combination technique. To represent user tendency, we defined and used loyalty, diversity and pioneerity and showed by experiments that our combination technique is useful. This combination technique improved the average coverage 23% and for the ceiling 40%.
Keywords
information filtering; content-based filtering; social filtering; collaborative filtering; demographic filtering; combination filtering;
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