期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于用户兴趣度的改进二部图随机游走推荐方法 被引量：4

IMPROVED RECOMMENDATION ALGORITHM OF BIPARTITE GRAPH RANDOM WALK BASED ON USER INTEREST DEGREE
下载PDF
导出
摘要 传统的二部图随机游走算法主要采用基于共同项目的相似度计算,并且项目之间、用户之间的影响程度是对称的,这种对称信息不能体现用户兴趣,推荐精度不高。为了提高推荐准确性,提出一种基于用户兴趣度的二部图随机游走方法。采用共同项目和用户打分项目数量的共同性质体现用户兴趣度,分析信息的不对称性,并在二部图中随机游走。实验表明,基于用户兴趣度的二部图随机游走算法提高了预测准确率和命中率。 Traditional bipartite graph random walk algorithm mainly uses the common projects-based similarity calculation,and the influ-ence degrees between both projects and users are symmetric.However,such symmetric information can not reflect user＆#39;s interest and the rec-ommendation accuracy is not high as well.In order to improve recommendation accuracy,this paper proposes a user interest degree-based bi-partite graph random walk algorithm.It adopts the common property of common projects and user rating projects number to reflect user interest degree and to analyse information asymmetry,and walks randomly in bipartite graph.Experiment shows that the user interest degree-based bi-partite graph random walk algorithm improves the prediction accuracy and hit rate.
作者 张怡文 王冉 程家兴
机构地区 安徽新华学院信息系统软件研究所
出处 《计算机应用与软件》 CSCD 2015年第6期76-79,共4页 Computer Applications and Software
基金 安徽省优秀青年基金项目(2012SQRL227) 国家级大学生创新训练计划项目(201212216034)
关键词 个性化推荐 二部图 兴趣度 随机游走 Personalised recommendation Bipartite graph Interest degree Random walk
分类号 TP391.6 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献19

  • 1许海玲,吴潇,李晓东,阎保平.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362. 被引量：544
  • 2Linden G,Smith B,York J.Amazon.com recommendations:Item-toitem collaborative filtering[J].Internet Computing,IEEE,2003,7(1):76-80.
  • 3Shardanand U,Maes P.Social information filtering:algorithms for automating“word of mouth”[C]//Proceedings of the SIGCHI conference on Human factors in computing systems.ACM Press/Addison-Wesley Publishing Co.,1995:210-217.
  • 4Claypool M,Gokhale A,Miranda T,et al.Combining content-based and collaborative filters in an online newspaper[C]//Proceedings of ACM SIGIR workshop on recommender systems.1999,60.
  • 5Jacobsson M,Rost M,Holmquist L E.When Media Gets Wise:collaborative filtering with mobile media agents[C]//Proceedings of the11th international conference on Intelligent user interfaces.ACM,2006:291-293.
  • 6Resnick P,Iacovou N,Suchak M,et al.Group Lens:an open architecture for collaborative filtering of netnews[C]//Proceedings of the1994 ACM conference on Computer supported cooperative work.ACM,1994:175-186.
  • 7Kim J W,Lee B H,Shaw M J,et al.Application of decision-tree induction techniques to personalized advertisements on internet storefronts[J].International Journal of Electronic Commerce,2001,5(3):45-62.
  • 8Debnath S,Ganguly N,Mitra P.Feature weighting in content based recommendation system using social network analysis[C]//Proceedings of the 17th international conference on World Wide Web.ACM,2008:1041-1042.
  • 9Zhou T,Ren J,Medo M,et al.Bipartite network projection and personal recommendation[J].Physical Review E,2007,76(4):046115-1-046115-7.
  • 10Liu J G,Zhou T,Che H A,et al.Effects of high-order correlations on personalized recommendations for bipartite networks[J].Physica A:Statistical Mechanics and its Applications,2010,389(4):881-886.

二级参考文献117

  • 1王辉,高利军,王听忠.个性化服务中基于用户聚类的协同过滤推荐[J].计算机应用,2007,27(5):1225-1227. 被引量：43
  • 2Shardanand U, Maes P. Social information filtering: Algorithms for automating "Word of Mouth". In: Proc. of the Conf. on Human Factors in Computing Systems. New York: ACM Press, 1995.210-217.
  • 3Hill W, Stead L, Rosenstein M, Furnas G. Recommending and evaluating choices in a virtual community of use. In: Proc. of the Conf. on Human Factors in Computing Systems. New York: ACM Press, 1995. 194-201.
  • 4Resnick P, Iakovou N, Sushak M, Bergstrom P, Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. In: Proc. of the Computer Supported Cooperative Work Conf. New York: ACM Press, 1994. 175-186.
  • 5Baeza-Yates R, Ribeiro-Neto B. Modern Information Retrieval. New York: Addison-Wesley Publishing Co., 1999.
  • 6Murthi BPS, Sarkar S. The role of the management sciences in research on personalization. Management Science, 2003,49(10): 1344-1362.
  • 7Smith SM, Swinyard WR. Introduction to marketing models. 1999. http://marketing.byu.edu/htmlpages/courses/693r/modelsbook/ preface.html
  • 8Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 2005,17(6):734-749.
  • 9Resnick P, Varian HR. Recommender systems. Communications of the ACM, 1997,40(3):56-58.
  • 10Balabanovic M, Shoham Y. Fab: Content-Based, collaborative recommendation. Communications of the ACM, 1997,40(3):66-72.

共引文献1068

同被引文献36

引证文献4

二级引证文献8

计算机应用与软件
2015年 第6期

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