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协同过滤在推荐系统中的应用研究 被引量：18

Research on Application of Collaborative Filtering in Recommender Systems
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摘要 本文介绍了协同过滤技术,分析了协同过滤在推荐系统中应用时所面临的问题,对主要的解决方法进行了 分类研究,最后,对本文研究进行了全面总结。
作者 王霞 刘琴
机构地区 上海金融学院信息管理及计算机应用系 上海华东政法学院信息技术中心
出处 《计算机系统应用》 2005年第4期24-27,共4页 Computer Systems & Applications
关键词 协同过滤技术 推荐系统 算法 数据稀疏性
分类号 TP393.09 [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

  • 1John S. Breese, David Heckerman, Car Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conf. on UAI-98,pp. 43 -52, San Francisco, July 24 -26 1998.
  • 2K. Yu, Z. Wen, X. Xu and M. Ester. Feature Weighting and Instance Selection for Collaborative Filtering.2nd International Workshop on Management of Information on the Web, in conjunction with the 12th International Conference on DEXA' 2001, Munich,Gerneny, 2001.
  • 3Kai Yu, Xiaowei Xu, Jianhua Tao, Martin Ester and Hans -Peter Kriegel. Instance selection techniques for memory - based collaborative filtering. In Proceedings of the second international conf. On data mining, part I visualization and applications,2002.
  • 4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

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94

引证文献18

二级引证文献83

计算机系统应用
2005年 第4期

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