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基于用户信息平滑聚类的协同推荐方法 被引量：2

Collaborative Recommendation Using Smoothing Clustering Based on User Information Matrix
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摘要 在电子商务中,协同推荐技术能够帮助用户发现感兴趣的东两。在协同推荐中,通常采用最近邻居的方法来产生推荐。随着商品数量的增多,协同推荐所需要的数据集也越来越稀疏,可用数据比例越来越少。为了解决这个问题,本文在传统的评分数据的基础上,引入用户的基本信息,对用户的基本信息进行离散化处理,将用户的基本信息转化成一个0、1的向量,在用户的信息的基础上计算最近邻居,根据最近邻居对用户缺失数据进行补充,在补充后的评分数据上进行聚类计算,并根据聚类结果对用户评分进行预测。实验表明引入用户的基本信息,并采用对基本信息离散化的处理方式进行缺失数据补充,在此基础上进行数据的聚类,能够提高预测评分的准确性。 Collaborative recommendation technology can help people find something interesting in the e-commerce business field.In collaborative recommendation,there is a common way to generate recommendation called nearest neighbor method.With the increase of commodity quantity,the ratio of useful data is decreasing.In order to solve the sparse problem,we collect and discrete user information on the basis of ordinary score data,then we convert user information to a 0-1 vector.We compute the N-nearest neighbors from the user information matrix and smooth the it using the k-NN.We cluster the user rating matrix to predict the score.The experiment results show that the approach of rating and discretion the user information can improves the predicting score precision.
作者 常富洋 许侃 林鸿飞
机构地区 大连理工大学计算机科学与技术学院
出处 《情报学报》 CSSCI 北大核心 2011年第8期796-801,共6页 Journal of the China Society for Scientific and Technical Information
基金 基金项目：国家自然科学基金资助项目（编号：60673039,60973068） 国家社科基金（编号：08BTQ025） 国家863高科技计划资助项目（编号：2006AA012151） 高等学校博士学科点专项科研基金资助课题（编号：20090041110002） 教育部留学回国人员科研启动基金项目和大连理工大学青年教师科研启动项目.
关键词 协同推荐 用户信息 数据平滑 评分聚类 collaborative recommendation user information data smoothing rating cluster
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1Goldberg D, Nichols D, Oki B M. Using collaborative filte- ring to weave an information tapestry[ J]. Communications of the ACM, 1992,35 (12) :61-70.
  • 2Resnick P, Iacovou N, Suchak M, et al. Grouplens: An open architecture for collaborative filtering of netnews [ A]//Proceedings of the ACM Conference on Computer- Supported Cooperative Work ( CSCW ' 94 ) [ C ]. Chapel Hill : ACM Press, 1994 : 175-186.
  • 3Shardanand U, Maes P. Social information filtering: Algor- ithms for automating "Word of Mouth" [ A ]//Proceedings of the ACM Conference on Human Factors in Computing Systems( CHI' 95 ) [ C ]. New York : ACM Press/Addison- Wesley Publishing Co. ,1995:210-217.
  • 4Hill W, Stead L, Rosenstein M, et al. Recommending and evaluating choices in a virtual community of use [ A ]// Proceedings of ACM Conference on Human Factors in Computing Systems ( CHI' 95 ) [ C ]. New York: ACM Press/Addison-Wesley Publishing Co. , 1995 : 194-201.
  • 5Breese J, Hecherman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering [ A ]// Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence ( UAI' 98 ) [ C ]. San Francisco: Morgan Kaufmann Publishers, 1998:43-52.
  • 6Xue G R, Lin C, Yang Q, et al. Scalable collaborative filtering using cluster-based smoothing[ A]//Proceedings of the 2005 ACM SIGIR Conference [ C ]. USA: ACM Press ,2005 : 114-121.
  • 7张海燕,丁峰,姜丽红.基于模糊聚类的协同过滤推荐方法[J].计算机仿真,2005,22(8):144-147. 被引量：25
  • 8Lain X N, Vu T, Le T D, et al. Addressing Cold-Start Problem in Recommendation Systems [ A ]//Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication ( ICUIMC ' 08 ) [ C ]. USA : ACM press, 2008 : 208-211.
  • 9Dougherty J, Kohavi R, Sahami M. Supervised and unsu- pervised discretization of continuous features [ A ]// Proceedings of the 12^th International Conference on Machine Learning ( ICML ' 95 ) [ C ]. California: ACM Press, 1995 : 194-202.
  • 10常富洋,林鸿飞,许侃.基于用户向量扩展的协同推荐方法[J].情报学报,2010,29(4):688-694. 被引量：9

二级参考文献28

  • 1孙小华,陈洪,孔繁胜.在协同过滤中结合奇异值分解与最近邻方法[J].计算机应用研究,2006,23(9):206-208. 被引量：30
  • 2Goldberg D,Nichols D,Oki B M,et al.Using collaborative filtering to weave aninformation tapestry[J].Communications of the ACM,1992,35(12):61-70.
  • 3Resnick P,Iacovou N,Suchak M,et al.Grouplens:An open architecture for collaborative filtering of netnews[C] //Proc.of the ACM CSCW'94 Conf.on Computer-Supported Cooperative Work.Chapel Hill:ACM,1994:175-186.
  • 4Shardanand U,Maes P.Social information filtering:Algorithms for automating "Word of Mouth"[C] //Proc.of the ACM CHI'95 Conf.on Human Factors in Computing Systems.New York:ACM Press/Addison-Wesley Publishing Co.,1995:210-217.
  • 5Hill W,Stead L,Rosenstein M,et al.Recommending and evaluating choices in a virtual community of use[C] //Proc.of the CHI'95.New York:ACM Press/Addison-Wesley Publishing Co.,1995:194-201.
  • 6Breese J,Hecherman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C] //Proc.of the 14th Conf.on Uncertainty in Artificial Intelligence (UAI'98).San Francisco:Morgan Kaufmann Publishers,1998:43-52.
  • 7Sarwar B M,Karypis G,Konstan J A,et al.Application of dimensionality reduction in recommender system-A case study[C] //Proc.of the ACM WebKDD 2000 Workshop.2000.http://robotics.stanford.edu/-ronnyk/WEBKDD2000/.
  • 8Dan Kal man.A Singularly Valuable Decomposition:The SVD of a Matrix[J].The CollegeMathematics Journal,1996,27 (1):2223.
  • 9Lam X N,Vu T,Le T D,et al.Proceedings of the 2nd international conference on Ubiquitous information management and communication,Addressing Cold-Start Problem in Recommendation Systems,2008.
  • 10Dougherty R K,Sahami M.Proceedings of the Twelfth International Conference,Supervised and unsupervised discretization of continuous features,1995.

共引文献46

同被引文献25

  • 1杨善林,李永森,胡笑旋,潘若愚.K-MEANS算法中的K值优化问题研究[J].系统工程理论与实践,2006,26(2):97-101. 被引量：191
  • 2Jain A K, Murty M N, Flynn P J. Data Clustering: A Review [ J ]. ACM Computing Surveys, 1999, 31 : 264-323.
  • 3Bezdek J C, Keller J M, Krishnapuram R, et al. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing[ C]. Kluwer, 1999.
  • 4Pal N R, Bezdek J C. On Cluster Validity for the Fuzzy C-means Model [ J ]. IEEE Transactions on Fuzzy Systems, 1995, 3 ( 3 ) : 370-379.
  • 5Kim M, Ramakrishna R S. New Indices for Cluster Validity Assessment [ J ]. Pattern Recognition Letters, 2005,26 (15) : 2353 - 2363.
  • 6Wang W, Zhang Y. On Fuzzy Cluster Validity Indices. Fuzzy Sets Systems, 2007, 158 (19) : 2095 -2117.
  • 7Bezdek J C. Fuzzy Mathematics in Pattern Classification. NY: Cornell University, 1974.
  • 8Bezdek J C. Numerical Taxonomy with Fuzzy Sets. Journal of Mathematical Biology, 1974, 7( 1 ) :57-71.
  • 9Xie X L,Beni G. A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8):841-847.
  • 10Kwon S H. Cluster Validity Index for Fuzzy Clustering. Electronics Letters, 1998, 34(22): 2176-2177.

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情报学报
2011年 第8期

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