期刊文献+

基于垂直网站的网络信息支持系统研究 被引量:7

Framework of Web Information Service System Based on Vertical Portal
下载PDF
导出
摘要 网络信息支持系统是指根据用户的偏好,为用户主动搜集、筛选和推送网上信息的主动式信息服务系统。讨论了网络信息支持系统的概念、作用,分析了基于垂直网站的信息支持系统的特点和逻辑功能,给出了系统的逻辑模型,对系统开发研制中的关键技术进行了研究。 The WISS(Web Information Service System) is an initiative service system,searching and downloading, filtering and pushing information for the users according to each of their preferences. In this paper the concept and significance of the ISS are discussed, the special features and logical functions of ISS based on vertical portal are analyzed, and the model of framework of the ISS is presented. The crucial techniques on system developing have been studied.
出处 《计算机应用研究》 CSCD 北大核心 2005年第7期105-107,共3页 Application Research of Computers
关键词 网络信息支持系统 垂直网站 搜索引擎 WEB使用挖掘 WISS(Web Information Service System) Vertical Portal Web Search Engine Web Usage Mining
  • 相关文献

参考文献9

  • 1甘仞初.管理信息系统[M].北京:机械工业出版社,2002..
  • 2Peter M, Louis P. Information Architecture for the World Wide Web(2nd Edition ) [M]. Sebastopol : O'Reilly Press,2002,28- 30.
  • 3Nan N, Eleni S, Mohammad E. Understanding Web Usage for Dynamic Web-Site Adaptivon: A Case Study [C]. Proceedings of the 4th International Workshop on Web Site Evolution( WSE'02), 11-14.
  • 4M Perkowitz,O Etzioni. Adaptive Web Sites: An AI Challenge[C].Proceedings of the 15th International Joint Conference on Artificial Intelligence,1997.
  • 5郝凤英.垂直网站与其信息服务模式[J].情报探索,2001(3):25-27. 被引量:6
  • 6曾春,邢春晓,周立柱.个性化服务技术综述[J].软件学报,2002,13(10):1952-1961. 被引量:396
  • 7Mobasher B,Cooley R, Srivastava J. Automatic Personalization Based on Web Usage Mining [J]. Communications of the ACM, 2000,43(8) :142-151.
  • 8M Perkowitz, O Etzioni. Adaptive Web Sites: Automatically Synthesizing Web Pages[C]. Proceedings of the 15th National Conference on Artificial Intelligence, 1998. 21-23.
  • 9M Perkowitz, O Etzioni. Towards Adaptive Web Sites: Conceptual Framework and Case Study [J]. Artificial Intelligence, 2000, 118 :245-275.

二级参考文献42

  • 1[4]http://www.ccidnet.com
  • 2Han, E.H., Boley, D., Gini, M., et al. WebACE: a web agent for document c ategorization and exploration. In: Sycara, K.P., Wooldridge, M., eds. Proceeding s of the 2nd International Conference on Autonomous Agents. New York: ACM Press, 1998. 408~415.
  • 3Schwab, I., Pohl, W., Koychev, I. Learning to recommend from positive evi dence. In: Riecken, D., Benyon, D., Lieberman, H., eds. Proceedings of the Inter national Conference on Intelligent User Interfaces. New York: ACM Press, 2000. 2 41~247.
  • 4Pretschner, A. Ontology based personalized search [MS. Thesis]. Lawrence, KS: University of Kansas, 1999.
  • 5Adomavicius, G., Tuzhilin, A. User profiling in personalization applicati ons through rule discovery and validation. In: Lee, D., Schkolnick, M., Provost, F., et al., eds. Proceedings of the 5th International Conference on Data Mining and Knowledge Discovery. New York: ACM Press, 1999. 377~381.
  • 6Balabanovic, M., Shoham, Y. Fab: content-based, collaborative recommendat ion. Communications of the ACM, 1997,40(3):66~72.
  • 7Sarwar, B.M., Karypis, G., Konstan, J.A., et al. Application of dimension ality reduction in recommender system--a case study. In: Jhingran, A., Mason, J.M., Tygar, D., eds. Proceedings of the ACM WebKDD Workshop on Web Mining for E -Commerce. New York: ACM Press, 2000.
  • 8Sarwar, B.M., Karypis, G., Konstan, J.A., et al. Analysis of recommendati on algorithms for e-commerce. In: Proceedings of the ACM Conference on Electroni c Commerce. New York: ACM Press, 2000. 158~167.
  • 9Breese, J.S., Heckerman, D., Kadie, C. Empirical analysis of predictive a lgorithms for collaborative filtering. In: Cooper, G.F., Moral, S., eds. Proceed ings of the 14th Conference on Uncertainty in Artificial Intelligence. San Franc isco: Morgan Kaufmann Publishers, 1998. 43~52.
  • 10Aggarwal, C.C., Wolf, J.L., Wu, K., et al. Horting hatches an egg: a new raph-theoretic approach to collaborative filtering. In: Chaudhuri, S., Madigan, D., Fayyad, U., eds. Proceedings of the ACM International Conference on Knowledg e Discovery and Data Mining. New York: ACM Press, 1999. 201~212.

共引文献406

同被引文献39

引证文献7

二级引证文献19

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