期刊文献+

基于矩阵分解的协同过滤算法 预览 被引量:37

Collaborative filtering algorithm based on matrix decomposition
在线阅读 下载PDF
收藏 分享 导出
摘要 协同过滤推荐算法是电子商务推荐系统中运用最成功的一种推荐技术。针对目前大多数协同过滤算法普遍存在的可扩展性和抗稀疏性问题,在传统的矩阵分解模型(SVD)的基础上提出了一种带正则化的基于迭代最小二乘法的协同过滤算法。通过对传统的矩阵分解模型进行正则化约束来防止模型过度拟合训练数据,并通过迭代最小二乘法来训练分解模型。在真实的实验数据集上实验验证,该算法无论是在可扩展性,还是在抗稀疏性方面均优于几个经典的协同过滤推荐算法。 Collaborative filtering recommendation algorithm is one of the most successful technologies in the e-commerce recommendation system.Aiming at the problem that traditional collaborative filtering algorithms generally exist sparseness resistance and extendibility,in this paper,a CF algorithm,alternating-least-squares with weighted-λ-regularization(ALS-WR) is described.That is,by using regularization constraint to the traditional matrix decomposition model to prevent model overfitting training data and using alternating-least-squares method to train the decomposition model.The experimental evaluation using two real-world datasets shows that ALS-WR achieves better results in comparison with several classical collaborative filter-ing recommendation algorithms not only in extendibility but also in sparseness resistance.
作者 李改 李磊 LI Gai1,2,3,LI Lei2,3 1.Shunde Polytechnic,Shunde,Guangdong 528333,China 2.School of Information Science and Technology,Sun Yat-Sen University,Guangzhou 510006,China 3.Software Institute,Sun Yat-Sen University,Guangzhou 510275,China
出处 《计算机工程与应用》 CSCD 北大核心 2011年第30期 4-7,共4页 Computer Engineering and Applications
基金 国家自然科学基金 中山大学高性能与网格计算平台资助
关键词 推荐系统 协同过滤 矩阵分解 迭代最小二乘法(ALS) 矩阵奇异值分解(SVD) recommended systems collaborative filtering matrix decomposition Alternating Least Square(ALS) Sigular Value Decomposition(SVD)
作者简介 李改(1981-),男,博士研究生,讲师,研究方向为数据挖掘、推荐系统; 李磊(1951-),男,博士,教授。E-mail:ligai999@126.com
  • 相关文献

参考文献18

  • 1Wu J L.Collaborative filtering on the Netflix prize dataset[D/EB]. http://dsec.pku.edu.cn/jinlong/. 被引量:1
  • 2Ricci F, Rokach L, Shapira B, et al.Recommender system hand- book[M].[S.l.] : Springer, 2011. 被引量:1
  • 3Adomavicius G, Tuzhilin A.Toward the next generation of rec- ommender systems:a survey of the state-of-the-art and possible extenstions[J].TKDE, 2005,17 (6): 734-749. 被引量:1
  • 4Bell R,Koren Y,Volinsky C.The bellkor 2008 solution to the Netflix prize[R].2007. 被引量:1
  • 5Paterek A.Improving regularized singular value decomposition for collaborative filtering[C]//KDD-Cup and Workshop.[S.l.]: ACM Press, 2007. 被引量:1
  • 6Lee D D,Seung H S.Learning the parts of objects by non-nega- tive matrix factorization[J].Nature,401:788-791. 被引量:1
  • 7徐翔,王煦法.基于SVD的协同过滤算法的欺诈攻击行为分析[J].计算机工程与应用,2009,45(20):92-95. 被引量:7
  • 8Pan R, Zhou Y, Cao B,et al.One-class collaborative filtering[C]// IEEE International Conference on Data Mining(ICDM),2008. 被引量:1
  • 9Pan R,Martin S.Mind the Gaps:weighting the unknown in large- scale one-class collaborative filtering[C]//Intemational Conference on Knowledge Discovery and Data Mining(KDD),2009. 被引量:1
  • 10Netflix.Netflix prize[EB/OL].htto://www.netflixprize.com. 被引量:1

二级参考文献36

  • 1陈健,印鉴.基于影响集的协作过滤推荐算法[J].软件学报,2007,18(7):1685-1694. 被引量:57
  • 2Resnick P,Iacovou N,Sushak M.GroupLens:An open architecture for collaborative filtering of netnews[C]//Proceedings of CSCW 1994, ACM SIG Computer Supported Cooperative Work,1994. 被引量:1
  • 3Sarwar B,KalTpis G,Konstan Let aLItem-based collaborative filtering recommendation algorithras[C]//Proc of the 10th International WorldWideWeb Conference,2001:285-295. 被引量:1
  • 4Mobasher B,Burke R,Sandvig J J.Model-based collaborative filtering as a defense against profile injection attacks[C]//Proceedings of the 21st National Conference on Artificial Intelligence(AAAI'06), 2006. 被引量:1
  • 5Lain S,Reidl J.ShiUing recommender systems for fun and profit[C]// Proceedings of the 13th International WWW Conference,New York, 2002. 被引量:1
  • 6Sawrar B M, Karypis G,Konstan J A.Application of dimensionality reduction in recommender systems-A case study[C]//ACM WebKDD 2000 Web Mining for E -Commerce Workshop, Boston, Massachusetts,July 16-20,2006. 被引量:1
  • 7Mobasher B,Burke R,Bhaumik R,et a.Effective attack models for shilling item-based collaborative filtering systems[C]//Proceedings of the 2005 Web KDD Workshop,Held in conjuction with ACM SIGKDD 2005, Chicago, Illinois, 2005. 被引量:1
  • 8Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information Tapestry[J]. Communications of the ACM,1992,35(12):61-70. 被引量:1
  • 9Resnick 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. 被引量:1
  • 10Shardanand U,Mages'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, 1995:210-217. 被引量:1

共引文献136

同被引文献295

引证文献37

二级引证文献58

投稿分析

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部 意见反馈