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基于数据挖掘的Web个性化信息推荐系统 被引量：12

Web Personalized Information Recommendation System Based on Data Mining
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摘要 基于数据挖掘的Web个性化信息推荐日益成为一个重要的研究课题。文章设计了一个基于数据挖掘的Web个性化信息推荐系统(WPIRS)。在WPIRS中,提出了推荐策略,在推荐策略中考虑针对不同类型的用户采用不同的推荐算法。根据用户是否有新颖信息的需求,WPIRS采用了两种推荐算法。 Web personalized information recommendation service based on data mining has become an important research task increasingly as the time goes by.This paper designs a Web Personalized Information Recommendation System（WPIRS） based on data mining.We put forward recommendation strategy in WPIRS,and different recommendation algorithms according different users.The system gives two kinds of recommendation algorithms.
作者 何波 王越
机构地区 重庆工学院计算机科学与工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2006年第3期178-179,186,共3页 Computer Engineering and Applications
基金 重庆市教委基础研究项目(编号:020612)资助
关键词 数据挖掘 推荐系统 个性化 WEB 信息推荐算法 用户 Data Mining,Personalized Information Recommendation ,user＇s transaction pattern
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] TP311.5 [自动化与计算机技术—计算机软件与理论]
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  • 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.
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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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计算机工程与应用
2006年 第3期

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