Discovering User Profiles for Web Personalized Recommendation

作     者：Ai-BoSong Mao-XianZhao Zuo-PengLiang Yi-ShengDong Jun-ZhouLuo 

作者机构：DepartmentofComputerScienceandEngineeringSoutheastUniversityNanjing210096P.R.China InformationSchoolShandongUniversityofScienceandTechnologyTaian271019P.R.China 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2004年第19卷第3期

页      面：320-328页

核心收录：

学科分类：08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 

基　　金：国家自然科学基金 

主　　题：web log user profile personalization generalized suffix tree clustering 

摘      要：With the growing popularity of the World Wide Web, large volume of useraccess data has been gathered automatically by Web servers and stored in Web logs. Discovering andunderstanding user behavior patterns from log files can provide Web personalized recommendationservices. In this paper, a novel clustering method is presented for log files called Clusteringlarge Weblog based on Key Path Model (CWKPM), which is based on user browsing key path model, to getuser behavior profiles. Compared with the previous Boolean model, key path model considers themajor features of users accessing to the Web: ordinal, contiguous and duplicate. Moreover, forclustering, it has fewer dimensions. The analysis and experiments show that CWKPM is an efficientand effective approach for clustering large and high-dimension Web logs.