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基于WWW缓存的用户长期兴趣发现 被引量：1

Discovering of User Long-term Interest Based on WWW Cache
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摘要 建立用户兴趣模型是实现个性化服务的关键技术之一。利用Web挖掘的方法,针对用户的兴趣变化,结合用户浏览Web页面的日期和相应Web页面特征项的词频,来建立用户长期和短期兴趣,并且通过模拟实验,验证该方法的有效性。
作者 索红光 杨涛
机构地区 中国石油大学(华东)计算机与通信工程学院
出处 《计算机系统应用》 2006年第12期59-61,共3页 Computer Systems & Applications
关键词 个性化服务 WEB挖掘 用户兴趣模型
分类号 TP393.092 [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

  • 1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：394
  • 2M J Martin -Bautista,et al.User profiles and fuzzy logic for web retrieval issues.Soft Computing 6(2003):365-372.
  • 3Kim,H.R.,Chan,P.K.(2003):Learning Implicit User Interest Hierarchy for Context in Personalization.
  • 4Sugiyama K,Hatano K,YoshikawaM.(2004):Adaptive Web Search Based on User Profile Construction without Any Effort from Users.

二级参考文献41

  • 1Han, 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.
  • 2Schwab, 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.
  • 3Pretschner, A. Ontology based personalized search [MS. Thesis]. Lawrence, KS: University of Kansas, 1999.
  • 4Adomavicius, 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.
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  • 6Sarwar, 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.
  • 7Sarwar, 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.
  • 8Breese, 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.
  • 9Aggarwal, 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.
  • 10Sarwar, B., Karypis, G., Konstan, J., et al. Item-Based collaborative fil tering recommendation algorithms. In: Shen, V.Y., Saito, N., eds. Proceedings of the 10th International World Wide Web Conference (WWW10). 2001. 285～295.

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计算机系统应用
2006年 第12期

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