摘要
在Web环境中,度量用户的浏览模式对Web站点结构的改进是有益的。挖掘和度量Web日志能够识别用户的访问模式模型,Web站点管理者能够应用这些模型研究用户的访问偏爱度,由此改进站点的体系结构以及分析这些改进带来的影响。因此,提出用户群偏爱度这样一个新概念,并使用了基于用户群的模糊聚类算法(UGFC),然后根据聚类结果,即具有相似访问习惯的用户群体,度量用户群偏爱度,再基于用户群偏爱度,利用混合阶Markov模型(HOMM)进行预测。实验表明,这种新的度量预测方法(UGFC-HOMM)比传统Markov模型(TMM)预测更准确,并且实验用精确率、覆盖率和运行时间这3个度量评价值对预测性能进行评估。
In the Web environment, measuring users＇ browsing patterns can benefit the improvement of framework of Web sites. Mining and measuring Web logs are able to identify users＇ navigation patterns models,Web-masters can apply these models for studying users＇ access favoritism to improve site organization and analyze the effects of changes to their Web sites.So,in this pa per,a new conception of user group favoritism is proposed,and Fuzzy Clustering algorithm based on User Group（UGFC） is used, and then, according to clustering result--user group having similar access habit,user group favoritism is measured ,based on which the Hybrid-Order Markov Model（HOMM） is used to predict.This new prediction metrics approach（UGFC-HOMM） shows that it is superior to Traditional Markov Model（TMM） in users＇ access prediction.Three evaluation metrics are applied to evaluate performance of prediction,namely,precision,coverage and runtime.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第10期209-212,共4页
Computer Engineering and Applications
