期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 27, 期 3, 页码 365-377出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2004.05.005
关键词
personalized recommendation; web usage mining; clustering; association mining; collaborative filtering; Web-based learning environment
Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users' browsing behavior. This research proposes a new personalized recommendation method integrating user clustering and association-mining techniques. Historical navigation sessions for each user are divided into frames of sessions based on a specific time interval. This research proposes a new clustering method, called HBM (Hierarchical Bisecting Medoids Algorithm) to cluster users based on the time-framed navigation sessions. Those navigation sessions of the same group are analyzed using the association-mining method to establish a recommendation model for similar students in the future. Finally, an application of this recommendation method to an e-learning web site is presented, including plans of recommendation policies and proposal of new efficiency measures. The effectiveness of the recommendation methods, with and without time-framed user clustering., are investigated and compared. The results showed that the recommendation model built with user clustering by time-framed navigation sessions improves the recommendation services effectively. (C) 2004 Elsevier Ltd. All rights reserved.
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