1) browsing preference paths
浏览偏爱路径
1.
Using concept of relative access rate—support-preference,a website access matrix and a website access time matrix are designed,and the user browsing preference paths are obtained by using the two matrices.
利用相对访问率——支持-偏爱度的概念,设计了网站访问矩阵及时间矩阵,并利用这两个矩阵得到用户浏览偏爱路径。
2) users preferred browsing patterns
用户偏爱浏览路径
4) navigation preference
浏览偏爱
1.
Considering user navigation interest by page navigating time and path choosing patterns, the concept of navigation preference and the algorithm for mining preferred navigation patterns are proposed.
针对当前的挖掘算法只是简单地把频繁访问路径作为用户浏览的兴趣路径的问题，充分地考虑了用户在页面上的浏览时间和在路径选择上表现出来的浏览偏爱，提出了基于远程代理数据收集的浏览偏爱模式挖掘算法。
5) browse route
浏览路径
1.
Considering that browse route,browse time,browse times and so on are the important factors to influence the accuracy of commendation,a dynamic collaboration filtering recommendation method based on Hidden Markov Model(HMM) was proposed.
考虑到用户浏览路径、时间、浏览次数都是影响推荐准确度的重要因素,提出一种基于隐马尔可夫模型(HMM)的动态协同过滤推荐方法。
6) preferred navigation tree
浏览偏爱树
1.
Based on this concept, the preferred navigation tree is presented for mining Web access patterns.
在分析如何准确反映Web浏览兴趣的基础上提出偏爱度的概念,并依据这个概念设计了基于用户浏览偏爱树的偏爱路径挖掘算法。
补充资料：浏览
粗略地看个大概：把有关资料都浏览了一遍。
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参考词条