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优化稀疏数据集提高协同过滤推荐系统质量的方法 预览 被引量:16

Optimization of sparse data sets to improve quality of collaborative filtering systems
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摘要 协同过滤是目前个性化推荐系统中效果较好的一种推荐技术。由于用户和项目数量的急剧增加,使得反映用户喜好信息的评分矩阵非常稀疏,严重影响了协同过滤技术的推荐质量。针对这一问题提出了综合均值优化填充方法,该方法相比较于缺省值法和众数法,考虑到了用户评分尺度问题,同时也不存在众数法中的"多众数"和"无众数"问题。在同一数据集上,通过使用传统的基于用户的协同过滤算法进行验证,表明此方法可以有效提高推荐系统的推荐质量。 Currently,the collaborative filtering is one of the successful and better personalized recommendation technologies that have been applied to the personalized recommendation systems.As the number of users and items increase dramatically,the score matrix which reflects the users' preference information is very sparse.The sparse matrix seriously affects the recommendation quality of collaborative filtering.To solve this problem,this paper presented a comprehensive mean optimal filling method.Compared to the default method and the mode method,this method has two advantages.First,the method takes account of user rating scale issues.Second,the method does not have the "multiple mode" and the "no mode" problems.On the same data set,using traditional user-based collaborative filtering to test the effectiveness of the method,and the results prove that the new method can improve the recommendation quality of recommendation systems.
作者 刘庆鹏 陈明锐 LIU Qing-peng,CHEN Ming-rui(College of Information Science and Technology,Hainan University,Haikou Hainan 570228,China)
出处 《计算机应用》 CSCD 北大核心 2012年第4期 1082-1085,共4页 journal of Computer Applications
基金 海南慧人公司项目(HNHR2011-1)
关键词 推荐系统 协同过滤 均值 众数 信息过载 recommendation system collaborative filtering mean value mode information overload
作者简介 作者简介:刘庆鹏(1986-),男,山东临沂人,硕士研究生,主要研究方向:软件工程; 通信作者电子邮箱mrchen@hainu.edu.cn.陈明锐(1960-),男,海南澄迈人,教授,主要研究方向:软件工程。’
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  • 1BORCHERS A,HERLOCKER J,KONSTAN J,et al.Ganging upon information overload[J].Computer,1998,31(4):106-108. 被引量:1
  • 2SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collab-orative filtering recommendation algorithms[C]//Proceedings of the10th International Conference on World Wide Web.New York:ACM Press,2001:285-295. 被引量:1
  • 3SCHAFER J B,KONSTAN J A,RIEDL J.Recommender systemsin e-commerce[C]//Proceedings of the 1st ACM Conference on E-lectronic Commerce.New York:ACM Press,1999:158-166. 被引量:1
  • 4SCHAFER J B,KONSTAN J A,RIEDL J.E-commerce recommen-dation applications[J].Data Mining and Knowledge Discovery,2001,5(1/2):115-153. 被引量:1
  • 5VARIAN R.Recommender systems[J].Communications of theACM,1997,40(3):56-58. 被引量:1
  • 6LEE K C,KWON S.Online shopping recommendation mechanismand its influence on consumer decisions and behaviors:A causalmap approach[J].Expert Systems with Applications:An Interna-tional Journal,2008,35(4):1567-1574. 被引量:1
  • 7SHARDANAND U.Social information filtering for music recommen-dation TP-94-04[R].Cambridge:MIT Media Laboratory,1994. 被引量:1
  • 8SHARDANAND U,MAES P.Social information filtering:Algorithmsfor automating“word of mouth”[C]//Proceedings of the 1995 ACMSIGCHI Conference on Human Factors in Computing Systems.NewYork:ACM Press,1995:210-217. 被引量:1
  • 9MALTZ D,EHRLICH K.Pointing the way:active collaborative fil-tering[C]//Proceedings of the SIGCHI Conference on Human Fac-tors in Computing Systems.New York:ACM Press,1995:202-209. 被引量:1
  • 10ADOMAVICIUS G,TUZHILIN A.Toward the next generation ofrecommender systems:A survey of the state-of-the-art and possibleextensions[J].IEEE Transactions on Knowledge and Data Engineer-ing,2005,17(6):734-749. 被引量:1

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