A Model for Analysis and Prediction of Web User's Behavior Using Web Mining Techniques

Nowadays, web-based services like E-Commerce and E-Banking make fundamental changes to the ways of using internet and human's life. Web shares direct media with low costs between services of businesses and their customers. Businesses need to record, study and analyze their user's behavior and interests in order to adapt content and interface of their web site with user's interest for targeted marketing and advertising and then complete the process of personalization. For this purpose and for analysis of users’ behavior and making recommendations based on the users’ behavior, web mining approaches can be used. In this paper, a model was developed which can be applied for analyzing and predicting user's behaviors of a specific web site. First, users were clustered with affinity propagation algorithm and then, their behaviors were analyzed using sequential pattern mining algorithm called CM-SPADE. In the next step, for each cluster, User's profile was created. Then by using these profiles, recommendations can be made for new users. At last, the represented model was evaluated and the final results was acceptable.