基于用户反馈与主题关联度的网页排序算法改进

针对传统PageRank算法存在主题漂移、忽略用户兴趣及偏向旧网页的问题,提出一种基于用户反馈与主题关联度的网页排序改进算法。该算法为了更好满足用户的检索需求,利用用户对链接的点击量、链接结构及网页浏览时间来构成用户反馈因子,同时结合网页内容的主题关联度因子,共同对网页PR值进行适当修正与合理分配。为了改善网页排序的效果,算法通过添加时间相关因子,对新网页作出一定补偿,使得新网页一定程度上浮,旧网页下沉。实验结果表明，所提算法在相同实验环境下，相对于传统PageRank算法，提升了用户搜索满意度平均值约2.1%，达到了优化网页排序效果的预期研究目标。

Improved PageRank algorithm based on user feedback and topic relevance

Concerning the problems that exist in traditional PageRank algorithm, such as topic drifting, neglecting user browsing interests and stressing on old Web pages, an improved PageRank algorithm was proposed. To satisfy user requirements better, factors of users' clicks to links, link structure, browser time on pages, topic relevance decided by contents and existing time of pages were taken into consideration. The experimental results show that compared with the traditional PageRank algorithm, the average value of users' degree of satisfaction has been promoted approximately by 2.1% with the proposed algorithm, and ranking results has been optimized in a certain extent.