期刊文献+

基于聚类协作过滤的商品个性化推荐系统的实现 被引量:2

Realization of commodity personal recommendation based on clustering collaborative filtering
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摘要 根据目前电子商务网站中商品个性化推荐的现状,本文提出对不同的商品可分别根据商品间的相似性和顾客间的相似性进行聚类和协作推荐,并具体给出基于聚类协作过滤的商品个性化推荐的流程、系统设计和系统实现。
作者 闵敏
出处 《制造业自动化》 北大核心 2010年第2期157-160,共4页 Manufacturing Automation
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参考文献4

  • 1闵敏.应用于顾客购物相似性聚类的Rock改进算法[J].扬州大学学报:自然科学版,2006,3:91-94.
  • 2邓爱林,左子叶,朱扬勇.基于项目聚类的协同过滤推荐算法[J].小型微型计算机系统,2004,25(9):1665-1670. 被引量:147
  • 3Badrul Sarwar,George Karypis,Joseph Konstan,John Reidl. Item-Based Collaborative Filtering Recommendation Algorithms[J]. Proceedings of the tenth international conference on World Wide Web.2003:285-295.
  • 4Margaret H.Dunham著,郭崇慧,田凤占,靳晓明,等译.数据挖掘教程,第1版[M].北京:清华大学出版社,2005.

二级参考文献18

  • 1Schafer J B, Konstan J A and Riedl J. Recommender systems in E-Commerce[C]. In: ACM Conference on Electronic Commerce(EC99), 1999, 158-166.
  • 2Breese J, Hecherman D and Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]. In:Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence(UAI-98), 1998, 43-52.
  • 3Schafer J B, Konstan J A and Riedl J. E-Commerce recommendation applications [J]. Data Mining and Knowledge Discovery,2001, 5 (1-2): 115-153.
  • 4Goldberg D, Nichols D, Oki B M and Terry D. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992,35(12):61-70.
  • 5Resnick P, Iacovou N, Suchak M, Bergstrom P and Riedl J.Grouplens. an open architecture for collaborative filtering of netnews[C]. In: Proceedings of ACM CSCW' 94 Conference on Computer-Supported Cooperative Work, 1994,175-186.
  • 6Shardanand U and Maes P. Social information filtering: algorithms for automating ''Word of Mouth'' [C]. In Proceedings of ACM CHI' 95 Conference on Human Factors in Computing Systems, 1995, 210-217.
  • 7Hill W, Stead L, Rosenstein M and Furnas G. Recommending and evaluating choices in a virtual community of Use[C]. In:Proceedings of CHI' 95, 1995,194-201.
  • 8Sarwar B, Karypis G, Konstan J and Riedl J. Item-based collaborative filtering recommendation algorithms[C]. In:Proceedings of the Tenth International World Wide Web Conference, 2001,285-295.
  • 9Chickering D and Hecherman D. Efficient approximations for the marginal likelihood of bayesian networks with hidden variables[J]. Machine Learning, 1997, 29, 181-212.
  • 10Dempster A, Laird N and Rubin D. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society, 1977, 38(1): 1-38.

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