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智能搜索引擎中用户兴趣模型分析与研究 被引量：32

Analysis and Research on the User Interest Profile in Intelligent Search Engine
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摘要 用户兴趣模型是智能搜索引擎系统中的重要组成部分。本文提出一种新的方法,以动态询问的方式建立初始用户兴趣模型,通过分析、学习用户浏览行为历史,动态更新用户兴趣模型,有效地解决了用户兴趣的自适应变化。 User interest profile is one of the important components of intelligent search engine. A new method is presented in this paper. It sets up the initial user interest profile by dynamic enquiry, and updates the profile according to user's access log. Consequently, it effectively solves the self-adapting changes of user's interests.
作者 蒋萍 崔志明
机构地区 苏州大学智能信息处理及应用研究所
出处 《微电子学与计算机》 CSCD 北大核心 2004年第11期24-26,共3页 Microelectronics & Computer
基金 江苏省自然科学基金项目(BK2002039)
关键词 智能搜索引擎 用户兴趣模型 页面访问挖掘 Intelligent search engine, User interest profile, Web usage mining
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献4

  • 1M J Martin-Bautista, et al. User profiles and fuzzy logic for web retrieval issues. Soft Computing 6(2003):365～372.
  • 2蔡登,卢增祥,李衍达.信息协同过滤[J].计算机科学,2002,29(6):1-4. 被引量：19
  • 3Liliana A, Luca C, Ilaria T. An adapitive system for the personalized access to news. AI Communications 2001, 14(3):129～147.
  • 4Cher L, Sycara K. A personal agent for browsing and searching. Proceedings of the 2nd international conference on autonomous agents and multi-agent system AGENT 98, New York ACM 1998:132～139.

二级参考文献29

  • 1Resnick P,et al. GroupLens: An open architecture for collaborative filtering of netnews. In: Proc. of 1994 Conf. on Computer Supported Collaborative Work, 1994. 175～ 186
  • 2Konstan J A ,et al. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 1997, 40(3) :77～87
  • 3Herlocker J L, et al. An algorithmic framework for performing collaborative filtering. In:Proc. of the 22nd annual intl. ACM SIGIR conf. on Research and development in information retrieval,1999
  • 4Shardanand U, Maes P. Social Information Filtering: Algorithms for Automating Word of Mouth. In:Conf. proc. on Human factors in computing systems (ACM CHI '95), Denver, 1995.210～217
  • 5Hill W,et al. Recommending and Evaluating Choices in a Virtual Community of Use. In:Proc. of ACM CHI'95 Conf. on human factors in computing systems, Denver, 1995. 194～201
  • 6Dahlen B J, et al. Jump-starting movielens: User benefits of starting a collaborative filtering system with "dead data". University of Minnesota:[TR 98-017]. 1998
  • 7Goldberg K,et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval Journal . 2000
  • 8Schafer J B, Konstan J A. Riedl J. Recommender systems in ecommerce. In: Proc. of the ACM Conf. on Electronic Commerce (EC-99). 1999. 158～166
  • 9Morita M ,Shinoda Y. Information filtering based on user behavior analysis and best match text retrieval. In :Proc. of the Seventeenth Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1994. 272～281
  • 10Terveen L,et al. PHOAKS: A System for Sharing Recommendations. Communications of the ACM, 1997,40(3): 59～62

共引文献18

同被引文献275

引证文献32

二级引证文献130

微电子学与计算机
2004年 第11期

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