Computer Science ›› 2017, Vol. 44 ›› Issue (7): 227-231.doi: 10.11896/j.issn.1002-137X.2017.07.040
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SUN Yan-ge, WANG Zhi-hai and HUANG Dan
[1] GANTZ J,REINSEL D.IDC:The digital universe in 2020:Big data,bigger digital shadows,and biggest growth in the far east .http://www.emc.com/leadship/digital-universe/2012view/index.htm. [2] SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques[J].Advances in Artificial Intelligence,2009,9(4):1-19. [3] DING Y,LI X.Time weight collaborative filtering[C]∥Proceedings of the 14th ACM International Conference on Information and Knowledge Management.New York,USA:ACM,2005:485-492. [4] GONG S J,CHENG G H.Mining user interest change for improving collaborative filtering[C]∥Proceedings of the 2008 Se-cond International Symposium on Intelligent Information Technology Application.Washington,USA:IEEE Computer Society,2008:24-27. [5] LEE T Q,PARK Y,PARK Y T.A time-based approach to effective recommender systems using implicit feedback[J].Expert Systems with Applications,2008,4(4):3055-3062. [6] ADOMAVICIUS 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,2005,7(6):734-749. [7] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Procee-dings of the 10th International Conference on World Wide Web.New York,USA:ACM,2001:285-295. [8] BREESE J S,HECKERMAN D,KADIE C.Empirical analysisof predictive algorithms for collaborative filtering[C]∥Procee-dings of the 14th Conference on Uncertainty in Artificial Intelligence.San Francisco,USA:Morgan Kaufmann Publishers,1998:43-52. [9] PAVLOV D,PENNOCK D.A maximum entropy approach to collaborative filtering in dynamic,sparse,high-dimensional domains[C]∥Proceedings of the 16th Annual Conference on Neural Information Processing Systems.MIT Press,2002:1441-1448. [10] ZHANG J W,YANG Z.Collaborative filtering recommendation algorithm based on improved user clustering[J].Computer Science,2014,41(12):176-178.(in Chinese) 张峻玮,杨洲.一种基于改进的层次聚类的协同过滤用户推荐算法研究[J].计算机科学,2014,41(12):176-178. [11] YIN H,CUI B,SUN Y,et al.LCARS:A Spatial Item Recommender System[J].ACM Transactions on Information Systems (TOIS),2014,2(3):1-37. [12] SALAKHUTDINOV R,MNIH A.Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C]∥ International Conference on Machine Learning.ACM,2008:880-887. [13] LEE J,KIM S,LEBANON G,et al.Local low-rank matrix approximation[J].Journal of Machine Learning Research,2013,8(2):82-90. [14] LEE J,BENGIO S,KIM S,et al.Local collaborative ranking[C]∥Proceedings of the 23rd International Conference on World Wide Web (WWW 2014).Springer,2014:85-96. [15] LIU T Y.Learning to rank for information retrieval[J].Foundations and Trends in Information Retrieval,2009,3(3):225-331. [16] LIU H Y,WANG Z H,HUANG D,et al.Listwise Collaborative Ranking Based on the Assumption of Locally Low-Rank Rating Matrix[J].Journal of Software,2015,6(11):2981-2993.(in Chinese) 刘海洋,王志海,黄丹,等.基于评分矩阵局部低秩假设的成列协同排名算法[J].软件学报,2015,26(11):2981-2993. [17] KOLDA T G,BADER B W.Tensor decompositions and applications [J].SIAM Review,2009,1(3):455-500. [18] SALAKHUTDINOV R,MNIH A.Bayesian probabilistic matrix factorization using markov chain monte carlo[C]∥Proceedings of the 25th International Conference on Machine Learning.New York,USA:ACM,2008:880-887. |
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