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一种基于用户反馈的时间感知推荐方法

User feedback based method for time-aware recommendations
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摘要 将社交网络的动态性和用户反馈信息融入到推荐方法中,提出一种基于用户反馈的时间感知推荐方法。该方法利用时间衰减因子对带有时间加权的动态社交网络进行兴趣衰减分析,使时间间隔较近用户的选择行为对资源对象的推荐作用获得较高的贡献度,体现用户兴趣的时间效应特性。扩展相似度计算方法,将用户反馈表示为正反馈信息和负反馈信息,考虑用户反馈信息对推荐方法的影响。通过在社交网络真实推荐数据集上的对比实验,结果表明该方法优于基于协同过滤的推荐方法。 By exploiting the context information, such as time, relationship and user feedback information in social network,a time-aware social recommendation method based on user feedback is proposed. The proposed method incorporates the temporal factors by introducing a time weight function, which models the decay of user interest. Moreover, the method considers the user positive feedback and negative feedback information, as well as the social relationship information for recommendation. Experimental results and analysis show that the proposed method outperforms the collaborative filtering method for top-k item recommendation in social networks.
出处 《计算机工程与应用》 CSCD 北大核心 2015年第22期141-144,180,共5页 Computer Engineering and Applications
关键词 协同过滤 时间感知推荐 用户反馈 推荐系统 社交网络 collaborative filtering time-aware recommendation user feedback recommender system social network
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参考文献17

  • 1Biancalana C, Gasparetti F, Micarelli A, et al.An approach to social recommendation for context-aware mobile ser- vices[J].ACM Trans on Intell Syst Technol,2013,4(1): 1-31.
  • 2Yang Xiwang, Steck H, Guo Yang, et al.On top-k recom- mendation using social networks[C]//Proceedings of the Sixth ACM Conference on Recommender Systems,2012: 67-74.
  • 3Adomavicius G, Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering, 2005, 17 (6) : 734-749.
  • 4Deshpande M, Karypis G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems, 2004,22(1) : 143-177.
  • 5王立才,孟祥武,张玉洁.上下文感知推荐系统[J].软件学报,2012,23(1):1-20. 被引量:179
  • 6Yang Shuanghong, Long Bo, Smola A J, et al.Collabora- tire competitive filtering:learning recommender using con- text of user choice[C]//Proceedings of the 34th Interna- tional ACM SIGIR Conference on Research and Devel- opment in Information Retrieval,2011:295-304.
  • 7Bakshy E,Hofman J M, Mason W A, et al.Everyone' s an influencer: quantifying influence on twitter[C]//Pro- ceedings of the Fourth ACM International Conference on Web Search and Data Mining,2011:65-74.
  • 8Sarwar B,Karypis G,Konstan J, et al.Item-based collab- orative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web, 2001:285-295.
  • 9Moghaddam S, Jamali M, Ester M, et al.FeedbackTrust: using feedback effects in trust-based recommendation systems[C]//Proceedings of the Third ACM Conference on Recommender Systems, 2009 : 269-272.
  • 10吴湖,王永吉,王哲,王秀利,杜栓柱.两阶段联合聚类协同过滤算法[J].软件学报,2010,21(5):1042-1054. 被引量:83

二级参考文献31

  • 1李蕊,李仁发.上下文感知计算及系统框架综述[J].计算机研究与发展,2007,44(2):269-276. 被引量:52
  • 2Xu HL,Wu X,Li XD,Yan BP.Comparison study of Internet recommendation system.Journal of Software,2009,20(2):350-362 (in Chinese with English abstract).http://www.jos.org.cn/1000-9825/3388.htm[doi:10.3724/SP.J.1001.2009.03388].
  • 3Marlin B.Collaborative Filtering:A machine learning perspective[MS.Thesis].Toronto:University of Toronto,2004.
  • 4Hofmann T.Latent semantic models for collaborative filtering.ACM Trans.on Information System,2004,22(1):89-115.[doi:10.1145/963770.963774].
  • 5Blei DM,Ng AY,Jordan MI.Latent Dirichlet allocation.Journal of Machine Learning Research,2003,3(3):993-1022.[doi:10.1162/ jmlr.2003.3.4-5.993].
  • 6Netflix update:Try this at home.2006.http://sifter.org/~simon/journal/20061211.html.
  • 7Zhang S,Wang WH,Ford J,Makedon F.Learning from incomplete ratings using non-negative matrix factorization.In:Ghosh J,ed.Proc.of the 6th SIAM Conf.on Data Mining.Bethesda:SIAM,2006.549-553.
  • 8Cheng YZ,Church GM.Biclustering of expression data.In:Bourne PE,ed.Proc.of the 8th Int'l Conf.on Intelligent Systems for Molecular Biology.La Jolla:AAAI Press,2000.93-103.[doi:10.1016/j.ipm.2008.12.004].
  • 9Cheng G,Wang F,Zhang CS.Collaborative filtering using orthogonal nonnegative matrix tri-factorization.Information Processing & Management,2009,45(3):368-379.
  • 10Shan HH,Banerjee A.Bayesian co-clustering.In:Altman R,ed.Proc.of the ICDM 2008.Washington:IEEE Computer Society Press,2008.530-539.

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