Automatic Filtering System for Analyzing Customer's Tastes

J.-Y. Shim (Korea)


Filtering system, incremental learning, Relative Importance, Personal preference


For making more intelligent system in the information so ciety it is necessary to implement the automatic filtering system which can extract the important information con sidering personal preference from a large amount data. Accordingly, in this paper we define Relative Impor tance (RI) factor as a cri-terion for representing the per sonal preference and propose Intelligent Information Filter ing system considering RI. This system is designed to have learning, perception inference and knowledge re-trieval and to have an incremental learning mechanism for the new obtained important information. This system is applied to the area for the analysis of customer’s tastes and its perfor mance is analyzed and compared.

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