关键词: 学习自动机, 网页排序, 超链接, 兴趣度因子

Abstract: Concerning the problems that exist in traditional PageRank algorithm, such as assign link weights equally and ignoring users’ interests, this paper proposes a PageRank algorithm based on learning automata and user interest（LUPR）. In the proposed method learning automata is assigned to each Web page which its function is determining the weight of hyperlinks between Web pages. Based on the analysis to users’ behavior, to measure the user’s interest in Web pages by user’s browsing behavior, this article takes users behavior into consideration to obtain the interest factor. The proposed algorithm calculates the rank of each Web page according to hyperlinks between Web pages and user interest measures the weights of each Web page. The simulation experiment results show that, compared with the traditional PageRank algorithm and WPR algorithm, the improved one achieves better page ranking accuracy and users’ satisfaction to some extent.

Key words: learning automata, page rank, hyperlinks, interest factor