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New Technology of Library and Information Service  2007, Vol. 2 Issue (6): 52-55    DOI: 10.11925/infotech.1003-3513.2007.06.12
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User Profile Mining of Combining Web Behavior and Content Analysis
Zhang Yulian   Wang Quan
(College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004,China)
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Abstract  

Facing massive information from Internet, this paper proposes a method that can acquire user interest profile and update user interest profile to realize the personalized information service based on user’s interest as well as possible. This method does not need to provide explicitly the information user’s interested in. It only needs the actions and contents when users visit and browse Web pages to obtain useful information, subsequently, using those information to establish and update user interest profile. This profile can describe user’s interest type and interest degree well and enhance the personalized information service efficiency.

Key wordsPersonalization      Search engine      User profile     
Received: 21 March 2007      Published: 25 June 2007
: 

TP391

 
Corresponding Authors: Zhang Yulian     E-mail: fyyuan@ysu.edu.cn
About author:: Zhang Yulian,Wang Quan

Cite this article:

Zhang Yulian,Wang Quan. User Profile Mining of Combining Web Behavior and Content Analysis. New Technology of Library and Information Service, 2007, 2(6): 52-55.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2007.06.12     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2007/V2/I6/52

1The Weeks Group, A Framework for Competitive Intelligence. http://www.weeksgroup.com/cisource/Free_Framework_For_CI.pdf(Accessed Mar.10,2007)
2Jansen B, Spink A,  Saracevic T. Real Life, Real Users, and Real Needs: A Study and Analysis of User Queries on the Web.Information Processing and Management,2000,36(2): 207-227
3Fox S. Pew Internet Project Data Memo. http://pewinternet.org/reports/toc.asp?Report=64(Accessed Mar.10,2007)
4Deolasee P, et al. Adaptive Push-Pull: Disseminating Dynamic Web Data,Proceeding of the 10th International WWW Conference.Hong Kong,2001:265-274
5Fuld & Company, Intelligence Software Report 2002. http://www.fuld.com(Accessed Mar.10,2007)
6海量智能分词研究版.http://www.hylanda.com/cgi-bin/download/download.asp?id=8(Accessed Mar.10,2007)
7计算所汉语词法分析系统ICTCLAS.http://www.nlp.org.cn/project/project.php?proj_id=6(Accessed Mar.10,2007)
8Negnevitsiky M. 人工智能:智能系统指南.北京:机械工业出版社,2006
9Liu L, et al. Information Monitoring on the Web: A Scalable Solution.World Wide Web Journal,2002,5(12): 263-304
10Boley D, Gini M, Gross R,  Han E,  Hastings K, Karypis G,  Kumar V,  Bamshad M,  Moore J. Document Categorization and Query Generation on the World Wide Web Using Webace. Journal of Artificial Intelligence Review,1999,13(5-6):365-391
11Pazzani M,  Muramatsu J,  Billsus D. Syskill & Webert: Identifying Interesting Web Sites.Proceedings of the 1996 National Conference on Artificial Intelligence (AAAI-96), Portland, 1996
12Godoy G, Amandi A. A User Profiling Architecture for Textual-Based Agents. Proceedings of the Fourth Argentine Symposium on Artificial Intelligence, Sante Fe, Argentina, 2002
13Douglis F, et al. The AT&T Internet Difference Engine: Tracking and Viewing Changes on the Web.World Wide Web, 1998,1(1):27-44
14电子政务工程服务网.http://www.echinagov.com/dzzw/ReadNews.asp?NewsID=9983(Accessed Mar.10,2007)
15Google の秘密 - PageRank 徹底解説. http://www.kusastro.kyoto-u.ac.jp/~baba/wais/pagerank.html(Accessed Mar.10,2007)
16庞剑锋,卜东波等.基于向量空间模型的文本自动分类系统的研究与实现.计算机应用研究, 2001,18(9):23-26

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