期刊文献+

一个基于Web访问路径聚类的智能推荐系统 被引量:1

An Intelligent Recommendation System Based on Web Navigation Path Clustering
下载PDF
导出
摘要 提出了一个基于Web用户访问路径聚类的智能推荐系统.系统使用基于代理技术的结构,由离线的数据预处理和基于用户访问路径的URL聚类以及在线推荐引擎两部分组成.提出了一个基于用户浏览兴趣的推荐规则集生成算法,在度量用户浏览兴趣时综合考虑了用户浏览时间和对该页面的访问次数.提出了一个基于推荐规则集和站点URL路径长度的URL推荐算法.实验表明,该算法比使用基于关联规则和基于用户事务的推荐算法的精确性有较大幅度的提高. An intelligent recommendation system is proposed based on clustering of Web user's navigation path. The system uses an architecture based on proxy techniques, and consists of two subsystems, i. e. , the offline subsystem, including data preparation and URL clustering based on user's browsing paths, and the online subsystem, including a recommendation engine and a Web HTYP server. A algorithm for generating recommendation rule set is proposed based on the user's browsing interest, which is measured by considering synthetically both the user's browsing time and the number of hits on the Web page. A recommendation algorithm is presented based on recommendation nile set and the length of Web site URLs. The experiments show that, comparing with the recommendation algorithms based on association rule or on user transaction, the algorithm precision is improved greatly.
出处 《信息与控制》 CSCD 北大核心 2007年第1期119-124,共6页 Information and Control
基金 国家自然科学基金资助项目(60173058)
关键词 路径聚类 智能推荐系统 用户浏览兴趣 推荐规则集 推荐引擎 navigation path clustering intelligent recommendation system user's browsing interest recommendation rule set recommendation engine
  • 相关文献

参考文献9

  • 1Srivastava J,Cooley R,Deshpande M,et al.Web usage mining:Discovery and applications of usage patterns from web data[J].SIGKDD Explorations,2000,1(2):12 -23.
  • 2Sarwar B,Karypis G,Konstan J,et al.Analysis of recommendation algorithm for E-commerce[A].Proceedings of the 2nd ACM Conference on Electronic Commerce[C].New York:ACM Press,2000.158 - 167.
  • 3Brin S,Page L.The anatomy of large-scale hypertextual web search engine[J].Computer Networks and ISDN Systems,1998,30(1 -7):107 - 117.
  • 4Pirolli P,Pitkow J E.Distribution of surfers' paths through the world wide web:Empirical characterization[J].World Wide Web,1999,2(1 -2):29 -45.
  • 5Billsus D,Pazzani M J.Learning collaborative information filters[A].Proceedings of the Fifteenth International Conference on Machine Learning[C].San Francisco,CA,USA:Morgan Kaufmann Publishers,1998.46 - 54.
  • 6Fu X,Budzik J,Hammond K J.Mining navigation history for recommendation[A].Proceedings of the 2000 International Conference on Intelligent User Interfaces[C].New York,USA:ACM Press,2000.106 - 112.
  • 7Mobasher B,Dai H H,Luo T,et al.Discovery of aggregate usage profiles for Web personalization[A].Proceedings of the Web Mining for E-Commerce Workshop[C].New York,USA:ACM Press,2000.255 -264.
  • 8Agawal R,Strikant R.Fast algorithm for mining association rules[A].Proceedings of the 20th International Conference on Very Large Data[C].New York,USA:ACM Press,1994.487-499.
  • 9Mobasher B,Dai H,Luo T,et al.Improving the effectiveness of collaborative filtering on anonymous Web usage data[A].Proceedings of IJCAI-01 Workshop on Intelligent Techniques for Web Personalization[C].Heidelberg,Germany:Springer-Verlag,2001.53-60.

同被引文献5

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部