基于Hadoop的用户搜索行为分析

用户搜索网页行为的分析是目前信息搜索的研究的热点,本文针对云计算中的并行计算搜索存在的检索速度慢,效率低等缺点提出了一种基于Hadoop海量用户搜索网页行为的方法,该方法主要是在网页PageRank算法的基础上,将用户影响因子,时间向量和网页相关性因素加入到算法中,使得改进后的PageRank算法得到了提高,进一步提高用户搜索网页行为的效率,实验中通过使用优酷实验室中的查询日志分析证明了本文的算法具有良好的效果,并对云计算中的用户行为分析具有一定的指导意义.

Analyzing Users' Searching Behavior Based on Hadoop

The analysis of users' behavior of searching Webpages is the hotspot of current information searching. This paper focus on the weakness in the parallel calculation search of cloud calculation, like slow research speed, low efficiency and so on, a method based on Hadoop for mass users to search Web-pages is proposed, in which users' impact factors, time vector and Web-related factors are added to the algorithm based on the PageRank algorithm so as to further improve the efficiency for users in searching Web-pages. Analysis of query log in Youku laboratory is used in the experiment to prove algorithm in this paper has good effect as well as some guiding significance for users' behavior analysis in cloud computing.