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Extracting Typical Group Preferences through User-Item Optimization and User Profiles in Collaborative Filtering System  

Ko Su-Jeong (인덕대학 컴퓨터 소프트웨어과)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.7, 2005 , pp. 581-591 More about this Journal
Abstract
Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.
Keywords
Extracting typical group preferences; collaborative user profiles; entropy; recommender system;
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Times Cited By KSCI : 2  (Citation Analysis)
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