Abstract
A multi-agent based automatic Web recommendation model is presented. The main objective of this work is to provide Web users with an autonomous navigating model that is able to relieve Web users from repetitive and tedious Web surfing. The proposed approach classifies Web pages through calculating weights of terms. A user's interest model and preference model are generated by analyzing the user's navigational history. Based on the contents of Web pages and a user's interest and preference models, Web pages are recommended to the user who is likely interested in the related topic. Moreover, an evaluation agent is employed, which aims to choose the trusted users and incorporates machine intelligence with human effort. In order to demonstrate the effectiveness of the proposed method, experiments are carried out. In the experiments, Web pages are classified and those pages that match a user's interests are recommended to the user.
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