从Web日志中挖掘用户浏览偏爱路径

Web日志中包含了大量的用户浏览信息，如何有效地从其中挖掘出用户浏览兴趣模式是一个重要的研究课题．作者在分析目前用户浏览模式挖掘算法存在的问题的基础上，利用提出的支持一偏爱度的概念，设计了网站访问矩阵，并基于这个矩阵提出了用户浏览偏爱路径挖掘算法：先利用Web日志建立以引用网页URL为行、浏览网页URL为列、路径访问频度为元素值的网站访问矩阵．该矩阵为稀疏矩阵，将该矩阵用三元组法来进行表示．然后，通过对该矩阵进行支持一偏爱度计算得到偏爱子路径．最后进行合并生成浏览偏爱路径．实验表明该算法能准确地反映用户浏览兴趣，而且系统可扩展性较好．这可以应用于电子商务网站的站点优化和个性化服务等．

Discovering Preferred Browsing Paths from Web Logs

Web logs contain a lot of user browsing information. How to mine user browsing interest patterns is a important research topic. On the analysis of the present algorithms for mining user broswing patterns, representing user broswing interest and intention accurately by comparing relatively access ratio and the average of relatively access ratio, support-preference can be used for mining user broswing paths. According to the conception, we proposed a User Access Matrix based preferred broswing paths algorithm. Firstly, An URL-URL matrix was set up from web logs according to Web site's broswing paths, where referer URL as rows, navigating URL as columns and path broswing frequency as matrix elements. This URL-URL matrix is a sparse matrix which can be represented by List of 3-tuples. Then, preferred broswing sub-paths could be discovered from the computation of this matrix. Finally, all the sub-paths were combined. Experiments showed that it was accurate and scalable. It's suitable for application in E-business, such as to optimize web site or to design personalized service.