基于Web的用户个性化服务研究
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摘要
随着Web信息资源的快速膨胀,往往使Web用户访问站点时无从下手或很难找到自己需要的准确信息。Web用户的个性化服务是针对不同的用户提供不同的Web信息和服务,不仅使用户快捷的访问Web站点,而且更方便用户快速的获取自身所需信息。
基于Web的用户个性化服务就是Web站点通过收集和分析用户的基本信息、浏览信息等知识,预测用户未来的网页请求,了解用户的兴趣爱好,分析用户的访问模式,根据用户的个性化需求,为用户实时的、主动的提供所需求的信息。用户个性化服务研究在增加Web站点信息浏览量及访问频度、优化站点结构等各方面都具有明显的效果。针对现有用户兴趣模型的不足,本文从用户基本信息、用户浏览的页面内容和用户的浏览行为三方面综合研究,为用户建立了个性化的用户兴趣模型,能够较好的为用户进行服务。
首先,本文介绍了Web数据挖掘、个性化服务、用户兴趣建模等基础知识,收集Web站点上注册用户留下的基本信息,利用概念层次技术对基本信息的属性进行概念提升,得到高度概括的数据库表,并将其转换成用户的特征需求,建立了基于概念层次树的用户需求规则,解决了用户的基本信息的忽略问题,为用户个性化服务提供了依据。
其次,通过详细分析和研究Web站点的用户浏览信息对个性化服务的影响,以及用户的浏览模式,定义了非注册用户对浏览信息的兴趣度,以兴趣度来度量用户的兴趣,并从用户浏览的页面内容和浏览行为两方面分别利用兴趣度挖掘出用户的兴趣爱好。
最后,以网上书店为背景,设计与实现了基于用户兴趣度模型的个性化服务系统。通过对网上书店的用户信息进行分析,为用户提供了完美的个性服务。实验验证了该方法的有效性。
With the rapid expansion of the Web information resources, when the Web users visits the site, they have no idea how to start it or it is often made hard to find their need accurate information. Web user personalized service can provide different information and services for different Web users, it not only allow users to access Web sites quickly, but also more quickly access to their required information.
The user personalized service based on Web , which can predict the future requested page, understand the user’s interest, analyze the user’s access patterns, and active to provide the related information based on the user’s individual needs, must collect and make an analysis of users’basic information, browsing information and other knowledge. The research of the user personalized service which increase the access to Web site information and the frequency of page views, be optimized the site structure and other areas have significant results. For the lack of the current user interest model, this article has a comprehensive study of the user’s basic information, the page content that user browsed web page, and the browsed behavior of the user, to create a personalized user interest model, and could provide better service for users.
Firstly, by understanding the basics knowledge of the Web data mining, personalization services, user interest modeling, Etc, the article collect the basic information of the registered users, enhance the concept of the properties of the basic information through the concept hierarchy technology. That can get highly summarized database table, which can convert the characteristics of the user requirements. The research has solved the ignored problem of the basic information of the user, and provided a basis for the user personalized service.
Secondly, it has defined the interest degree of the browsing information of the non-registered user, through the detailed analysis and research of the impact of personalized service with the Web users browsing information. The interest degree is used to measure the user’s interests, and mine the user’s interests from the page content that user browsed web page and the browsed behavior of the user.
Finally, with the background of the online bookstore, it designed and accomplished the personalized service system that based on the user interest model. The system can make an analysis of the user information of the online bookstore, and has provided users with the perfect personalized service. The experimental results demonstrated the effectiveness of the method.
基于Web的用户个性化服务就是Web站点通过收集和分析用户的基本信息、浏览信息等知识,预测用户未来的网页请求,了解用户的兴趣爱好,分析用户的访问模式,根据用户的个性化需求,为用户实时的、主动的提供所需求的信息。用户个性化服务研究在增加Web站点信息浏览量及访问频度、优化站点结构等各方面都具有明显的效果。针对现有用户兴趣模型的不足,本文从用户基本信息、用户浏览的页面内容和用户的浏览行为三方面综合研究,为用户建立了个性化的用户兴趣模型,能够较好的为用户进行服务。
首先,本文介绍了Web数据挖掘、个性化服务、用户兴趣建模等基础知识,收集Web站点上注册用户留下的基本信息,利用概念层次技术对基本信息的属性进行概念提升,得到高度概括的数据库表,并将其转换成用户的特征需求,建立了基于概念层次树的用户需求规则,解决了用户的基本信息的忽略问题,为用户个性化服务提供了依据。
其次,通过详细分析和研究Web站点的用户浏览信息对个性化服务的影响,以及用户的浏览模式,定义了非注册用户对浏览信息的兴趣度,以兴趣度来度量用户的兴趣,并从用户浏览的页面内容和浏览行为两方面分别利用兴趣度挖掘出用户的兴趣爱好。
最后,以网上书店为背景,设计与实现了基于用户兴趣度模型的个性化服务系统。通过对网上书店的用户信息进行分析,为用户提供了完美的个性服务。实验验证了该方法的有效性。
With the rapid expansion of the Web information resources, when the Web users visits the site, they have no idea how to start it or it is often made hard to find their need accurate information. Web user personalized service can provide different information and services for different Web users, it not only allow users to access Web sites quickly, but also more quickly access to their required information.
The user personalized service based on Web , which can predict the future requested page, understand the user’s interest, analyze the user’s access patterns, and active to provide the related information based on the user’s individual needs, must collect and make an analysis of users’basic information, browsing information and other knowledge. The research of the user personalized service which increase the access to Web site information and the frequency of page views, be optimized the site structure and other areas have significant results. For the lack of the current user interest model, this article has a comprehensive study of the user’s basic information, the page content that user browsed web page, and the browsed behavior of the user, to create a personalized user interest model, and could provide better service for users.
Firstly, by understanding the basics knowledge of the Web data mining, personalization services, user interest modeling, Etc, the article collect the basic information of the registered users, enhance the concept of the properties of the basic information through the concept hierarchy technology. That can get highly summarized database table, which can convert the characteristics of the user requirements. The research has solved the ignored problem of the basic information of the user, and provided a basis for the user personalized service.
Secondly, it has defined the interest degree of the browsing information of the non-registered user, through the detailed analysis and research of the impact of personalized service with the Web users browsing information. The interest degree is used to measure the user’s interests, and mine the user’s interests from the page content that user browsed web page and the browsed behavior of the user.
Finally, with the background of the online bookstore, it designed and accomplished the personalized service system that based on the user interest model. The system can make an analysis of the user information of the online bookstore, and has provided users with the perfect personalized service. The experimental results demonstrated the effectiveness of the method.
引文
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[2]朱志国. Web用户使用模式与兴趣挖掘方法研究[D].辽宁:大连理工,2010.
[3] Mobasher B, Dai H, et al. Effective Personalization Based on Association Rule Discovery from Web Information and Data Management (WIDM01)[DB/OL], Atlanta, Georgia, USA, NOV.2001.
[4] P.Cheeseman, J.Stutz. Baysian Classification (AutoClass): Theory and Results. In U. M. Fayyad , G. Piatetsky -Shapiro, P. Smyth Editors, Advances in Knowledge Discovery and Data Mining, AAAI Press,1996,153~180.
[5] McDonald D W. Ubiquitous Recommendation System. IEEE Computer, 2003, 36(10):111~112.
[6] Chen L., Sycara K., Webmate-Personal Agent for Browsing and Searching. In:Proceedings of the Second International Conference on Autonomous Agents[R], St.Paul, MN, May, ACM Press, New York, 1988:132~139.
[7] WUM: A Web Utilization Miner. http://wum.wiwi.hu-berlin.de/2001 [DB/OL]. Humboldt University Berlin.
[8] Yu P. Data Mining and Personalization Technologies [J]. In: Database System for Advanced Applications (DASFAA99).Taiwan,1999,6~13.
[9]海脉经济资讯.几个Web数据挖掘方向的公司介绍[J].网络经济周刊,http//www.hermes.orm.on. 2001,(62).
[10] Diao Y, Lu H, Chen S, et al. Toward Learning Based Web Query Processing. In: Proc. of 26th VLDB2000[C].Cairo, Egypt, 2000.317~328.
[11] Google公司. Google Suggest[EB/OL]. http//www.google.com. 2008.
[12]陈永平.基于WEB挖掘的个性化服务[D].安徽:合肥工业大学,2007.
[13]潘金贵,胡学联,李俊,等.一个个性化的信息搜索Agent的设计与实现[J].软件学报,1999,10(8):839-842.
[14]汪晓岩,胡庆生,李斌,等.面向Internet的个性化智能信息检索[J].计算机研究与发展,1999,36(9):1039~1046.
[15]霍达.基于WEB挖掘的个性化技术研究[D].河北:燕山大学,2005.
[16] Fayyad U.M., Piatetsky-Shapiro G., Smyth P.. Advances in knowledge discovery and data mining. AAAI/MIT Press,1996:153~180.
[17]张国权.基于Web数据挖掘的网站个性化服务研究[D].甘肃:西北师范大学,2007.
[18]陈安,陈宁,周龙骧,等.数据挖掘技术与应用[M].北京:科学出版社,2006.
[19]李雄飞,李军.数据挖掘与知识发现[M].北京:高等教育出版社,2003.
[20] Agrawal A., Faloutsos C., Swami A., Efficient similarity search in sequence database[R]. In FODO Conference. Evaston.Illinois, October 1993.
[21]田凤珍,韩宪忠,陈晨,等.基本概念层次树的用户特征挖掘技术[J].微型机与应用,2011,30(325):95~97.
[22] Cooley R., Mobasher B., Srivastava J.. Web mining:Information and pattern discovery on the world wide web[R]. In Intennational Conference on Tools with Artificial Intelligence. Newport Beach, IEEE,1997:558~567.
[23]段云棋.基于Web的个性化定制系统研究[D].天津:天津大学,2007.
[24]陈抒然.面向个性化服务的用户兴趣建模及应用研究[D].重庆:重庆大学,2007.
[25]林蔚.基于互联网的个性化信息服务优化研究[D].吉林:吉林大学,2008.
[26]裴仰军.个性化服务中用户兴趣模型的研究[D].重庆:重庆大学,2005.
[27]罗霞. Web挖掘中用户模型研究[D].四川:四川师范大学,2009.
[28] Lieberman H., Letizia : an agent that assists web browsing[R]. In:Burke R.,ed.Proceedings of the International Joint Conference on Artificial Intelligenece. Menlo Park,CA:AAAI Press, 1955:924~929.
[29] Kurt D.Bollaeker, Steve Lawrence, C.LeeGiles. Discovering relevant scientific literature on the web[J]. IEEE Intelligent Systems, 2000,15(2):42~47.
[30] Pazzani M., Muramatsu J., Billsus D.. Syskill&Webert: Identifying Interesting Web Sites[R]. Proceeding of the 13th National Conference on Artificial Intelligenece. 1996:54~61.
[31] Schwab I., Pohl W., Koychev I.. Learning to Recommend from Positives Evidence[R]. In: Proceedings of the International Conference on Intelligent User Interface. New York: ACM Press,2000:241~247.
[32]林鸿飞,杨元生.用户模型的表示和更新机制[J].计算机研究与发展,2002,39(7):844~847.
[33]应晓敏,刘明,窦文华.一种面向个性化服务的客户端细粒度用户建模方法[J].计算机工程与科学,2003,25(6):35~42.
[34]孙华梅,郭茂祖,焦杰,等.一种新的面向属性归纳中概念层次技术研究[J].管理科学学报,2004,7(1):65~72.
[35]王丽菊.面向属性归纳的特征规则挖掘方法的改进[J].鞍山科技大学学报,2004,27(6):443~445.
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