摘要
针对传统单纯聚类算法实现网页推荐精确度欠缺的问题,提出一种基于Web日志挖掘的个性化网页推荐模型,并实现了相应的网页推荐算法,算法结合聚类分析和关联规则挖掘,能有效实现网页推荐.实验结果表明,在保障网页页面推荐覆盖率的条件下,该方法有较高的精确度、有效性和实用性.
For the traditional Web recommendation based on clustering algorithms has low recommend accuracy,a Web recommended model based on Web log mining was proposed,and a main algorithm combined with fuzzy cluster and association rule mining was presented to realize the model.Experiments show the model and the algorithm keep the Web recommending covering rate and also have a higher accuracy.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2013年第2期267-272,共6页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61163052
61073009
60873235)
国家高技术研究发展计划863项目基金(批准号:2011AA010101)
国家重点基础研究发展计划973项目基金(批准号:2009CB320706)
教育部新世纪优秀人才支持计划项目(批准号:NCET-06-0300)
关键词
网页推荐
模糊聚类
关联规则挖掘
WEB日志挖掘
Web page recommendation
fuzzy clustering
association rules mining
Web log mining
