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

摘要： 针对传统PageRank算法存在的平分链接权重和忽略用户兴趣等问题，提出一种基于学习自动机和用户兴趣的页面排序算法LUPR。在所提方法中，给每个网页分配学习自动机，其功能是确定网页之间超链接的权重。通过对用户行为进一步分析，以用户的浏览行为衡量用户对网页的兴趣度，从而获得兴趣度因子。该算法根据网页间的超链接和用户对网页的兴趣度衡量网页权重计算每个网页的排名。最后的仿真实验表明，较传统的PageRank算法和WPR算法，改进后的LUPR算法在一定程度上提高了信息检索的准确度和用户满意度。

关键词: 学习自动机, 网页排序, 超链接, 兴趣度因子