Generation of Web Knowledge Flow for Personalized Services

Abstract

Personalized services provide specific services to satisfy user’s real-time requirements. How to effectively discover and organize proper Web resources is a key issue. Based on Web Knowledge Flow which is used to semantically organize and represent Web resources that are recommended to user, this paper presents a 3-way strategy of finding proper resources for a user in multi-user environment. With this strategy, generation of Web Knowledge Flow can be accomplished based on collaborative user and Semantics Link Network. First, according to the similarity degree between active user and collaborative user, collaborative user is refined to strict kind and loose kind. Secondly, user’s browsing sequence of Web resources is presented to find collaborative users and implemented in a 3-D coordinate space. Thirdly, average information entropy of semantic relationship between Web resources is presented to evaluate the similarity of users’ browsing feature. The experimental results demonstrate the validity of the method. It can be seen that the proposed method has a brilliant perspective in the applications of Web personalized services.