A study on the customer behavior tracking model based on temporal description logic in the process of E-Commerce

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.