Collaborative Filtering for a Distributed Smart IC Card System
Collaborative filtering, often used in E-commerce applications, is a method to cluster similar users based on their profiles, characteristics or attitudes on specific subjects. This paper proposes a novel method to implement dynamic collaborative filtering by Genetics-based machine learning, in which we employ Learning Classifier Systems extended to multiple environments. The proposed method is used in a yet another mobile agent system: a distributed smart IC card system. The characteristics of the proposed method are summarized as follows: (1) It is effective in distributed computer environments with PCs even for small number of users. (2) It learns users’ profiles from the individual behaviors of them then generates the recommendation and advices for each user. (3) The results are automatically accumulated in a local system on a PC, then they are distributed via smart IC cards while the users are interacting with the system. The method has been implemented and validated in Group Trip Advisor prototype: a PC-based distributed recommendor system for travel information.
KeywordsCollaborative Filter Learn Classifier System Rule Exchange Digital City Mobile Agent System
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