1) browsing interest paths
浏览兴趣路径
1.
After the definition of these two concepts,a browsing interest paths mining algorithm was devised according to the path-selection interest matrix and the page-browsing interest matrix.
设计了路径选择兴趣矩阵和页面浏览兴趣矩阵,并基于两个矩阵,设计了浏览兴趣路径挖掘算法。
2) browse interest
浏览兴趣
1.
Web Using schema mining gets the interesting schema from the data the user browsed,and apprehend the user s browse interest behavior,in order to improve the Website s structure or provide individual service for the user.
Web使用模式挖掘是从用户浏览网站的数据中抽取感兴趣的模式,理解用户的浏览兴趣行为,以便进一步改善网站结构或为用户提供个性化的服务。
3) Browsing interest
浏览兴趣度
4) 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。
5) user traversal interest
用户浏览兴趣度
1.
The concept of user traversal interest and the algorithm for mining interesting navigation patterns is proposed.
实验证明该算法是有效的,在用户浏览兴趣度量方面比当前的频繁访问路径挖掘算法更准确。
6) web access path
Web浏览路径
1.
Association rule-based similarity analysis for web access path;
本文提出的基于关联规则的Web浏览路径相似程度分析方法较好地解决了这个问题,通过实验也验证了算法的正确性。
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