2) 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。
3) Navigation prediction
浏览预测
1.
A new model of user navigation prediction based on collaborative filtering,UNCPM,is proposed in this paper.
该模型可以应用在大型电子商务网站的用户浏览预测上。
4) 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。
5) frequent browsing paths
频繁浏览路径
1.
Mining frequent browsing paths based on Web-log;
基于Web-log的频繁浏览路径挖掘
2.
This algorithm first solves interest by the linear regression method,then takes it and page name as the basic element, establishes web browsing tree which can display completely continual and iterative browsing paths in web logs,finally carries on analysing and mining frequent browsing paths on the web browsing tree.
首先以线性回归方法求解兴趣度,其次将此兴趣度和页面名称作为最基本要素,建立的web浏览树,此浏览树可以完整地表现出web日志中连续、重复的浏览路径,最后在web浏览树上进行分析挖掘频繁浏览路径。
补充资料：发育进度预测法(见发生期预测)
发育进度预测法(见发生期预测)
发育进度预测法见发生期预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条