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It is the basic way to enhance service ability to predict customer behavior in the process of E-commerce activities. Through applying temporal description logic (TL-ALCF) in describing the rules of time-dependent customer behavior change, this article puts forward a method in acquiring algorithms through improving the temporal correlated rules of the FP-tree(frequent pattern tree), and further constructs a model with high reasoning capacity being used in tracking customers' behavior. This model adopts the LFE-method (learning from examples) in obtaining rules dynamically from the sample behavior database, building up the database of behavior rules, and finally designing a reasoning engine functioning under the obtained rule sets.
Date of Conference: 19-20 May 2012