Abstract: This paper combines the Web logs and users browsing behavior to mine user browsing interest patterns. This paper establishes three matrixes which elements are the average visit times（divided by the number of characters in the website） and the frequency of visits and the number of users to pull scroll. Preferred browsing sub-paths will be discovered from the computation of this matrix. All the sub-paths are combined to generate a set of user preferred browsing paths. Experiments shows that the algorithm is feasible and effective for e-commerce site optimization and meaningful implementation of personalized service.

摘要： 使用Web日志与用户浏览行为相结合的方式对用户浏览兴趣模式进行挖掘。分别建立以访问次数、平均到网页中字符数的访问时间和拉动滑动条次数为元素值的矩阵，通过对矩阵进行路径兴趣度的计算得到兴趣子路径，进行合并生成用户兴趣路径集。实例分析表明该算法是可行和有效的，对于电子商务网站的优化和实施个性化服务具有意义。