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A Recommender Agent using Association Item Trees  

Ko, Su-Jeong (인덕대학 컴퓨터소프트웨어과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.4, 2009 , pp. 298-305 More about this Journal
Abstract
In contrast to content_based filtering systems, collaborative filtering systems not only don't contain information of items, they can not recommend items when users don't provide the information of their interests. In this paper, we propose the recommender agent using association item tree to solve the shortcomings of collaborative filtering systems. Firstly, the proposed method clusters users into groups using vector space model and K-means algorithm and selects group typical rating values. Secondly, the degree of associations between items is extracted from computing mutual information between items and an associative item tree is generated by group. Finally, the method recommends items to an active user by using a group typical rating value and an association item tree. The recommender agent recommends items by combining user information with item information. In addition, it can accurately recommend items to an active user, whose information is insufficient at first rate, by using an association item tree based on mutual information for the similarity between items. The proposed method is compared with previous methods on the data set of MovieLens recommender system.
Keywords
Association item tree; recommender agent; collaborative filtering system;
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Times Cited By KSCI : 2  (Citation Analysis)
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