参考文献/References:
[1]ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE Trans on Knowledge and Data Engineering, 2005,17(6):734-749.
[2]RESNICK P, VARIAN H R. Recommender system[J]. Communication of the ACM, 1997, 40(3):56-58.
[3]赵良辉,熊作贞. 电子商务推荐系统综述及发展研究[J]. 电子商务, 2013, 35(12):58-60.
[4]崔春生,吴祁宗,王莹.用于推荐系统聚类分析的用户兴趣度研究[J]. 计算机工程与应用,2011, 47(7):226-228.
[5]GOLDBERG D, NICOLS D. Using collaborative filtering to weave an information tapestry[J].Communications of the ACM, 1992,35(12):61-70.
[6]刘发升, 洪营. 基于用户特征属性和云模型的协同过滤推荐算法[J]. 计算机工程与科学, 2014, 36(6):1172-1176.
[7]许海玲. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2):350-362.
[8]孙光福, 吴乐, 刘淇, 等.基于时序行为的协同过滤推荐算法[J]. 软件学报, 2013, 24(11):2721-2733.
[9]RESNICK P, IAKOVOU N, SUSHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews[C]∥Proceeding of the 1994 Computer Supported Cooperative Work Conference.North Carolina:ACM, 1994:175-186.
[10]LINDEN G, SMITH B, YORK J. Recommendations tem-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1):76-80.
[11]SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//International World Wide Web Conferences. Hongkong:ACM,2001:285-295.
[12]DESHOANDE M, KARYPIS G. Item-based top-n recommendation algorithms[J]. ACM Trans Information System, 2004, 22(1):143-177.
[13]张光卫,李德毅,李鹏,等.基于云模型的协同过滤推荐算法[J]. 软件学报, 2007, 18(10):2403-2411.
[14]黄创光, 印鉴, 汪静, 等.不确定近邻的协同过滤推荐算法[J]. 计算机学报, 2010,33(8):1369-1377.
[15]刘庆鹏, 陈明锐. 优化稀疏数据集提高协同过滤推荐系统质量的方法[J]. 计算机应用, 2012, 32(4):1082-1085.
[16]CHIEN Y H, GEORGE E I. A Bayesian model for collaborative filtering[C]∥Proceeding of the Steventh International Workshop Artificial Intelligence and Statistics.Florida:[s.n.], 1999.
[17]GETOOR L, SAHAMI M. Using probabilistic relational models for collaborative filtering[C]∥Proceeding of the Workshop Web Usage Analysis and User Profiling(WEB KDD’99). San Diego:[s.n.], 1999.
[18]PAVLOV D, PENNOCK D. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains[C]∥International Conference on Neural Information Processing.Cambridge:MIP Press,2002:1465-1472.
[19]UNGAR L H, FOSTER D P. Clustering methods for collaborative filtering[C]//Proceedings of the 1998 workshop on Recommen Dation Systems.Menlo Park:AAAI Press,1998:84-88.
[20]常璐. 高校图书馆E-learning支持服务研究[D]. 上海:东华大学, 2013.
[21]SALTON G. Automatic text processing: the transformation, analysis, and retrieval of information by computer[M]. Boston: Addison-Wesley, 1989.
[22]刘玲. 基于Topsis思想的内容推荐算法研究[J]. 数学的实践与认识, 2012, 42(16):113-119.
[23]BALABANOVIC M, SHOHAM Y. Fab: content-based collaborative recommendation[J]. Communications of the ACM, 1997, 40(3):66-72.
[24]蔡红蕾. 二部图网络结构算法在推荐系统中的应用[D]. 秦皇岛:燕山大学,2014.
[25]ZHOU T, JING L L, SU R Q, et al. Effect of initial configuration on network-based recommendation[J]. Europhys Lett,2008,81(5):58004.
[26]肖波, 徐前方, 蔺志青, 等. 可信关联规则及其基于极大团的挖掘算法[J]. 软件学报, 2008, 19(10):2597-2610.
[27]PINTO H, HAN J, PEI J, et al. Multi-dimensional sequential pattern mining[C]∥Conference on Information and Knavledge Management. Atlanta: ACM, 2001: 81-88.
[28]杨红菊, 梁吉业. 一种有效的关联规则的挖掘方法[J]. 计算机应用, 2004, 24(3):88-89.
[29]殷红, 许彦如, 王长波. 考虑信誉的网络交易可视化研究[J]. 东华大学学报(自然科学版), 2013, 39(4):514-518.
[30]黄仁, 孟婷婷. 个性化推荐算法综述[J]. 中小企业管理与科技(中旬刊), 2015(8):271-273.
[31]项亮. 推荐系统实战[M]. 北京:人民邮电出版社, 2012:151-152.
[32]王国霞, 刘贺平. 个性化推荐系统综述[J]. 计算机工程与应用, 2012, 48(7):66-76.
[33]PAZZANI M, BILLSUS D. Learning and revising user profiles: The identification of interesting Web sites[J]. Machine Learning, 1997, 27(3):313-331.
[34]ZHOU T, REN J, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Physical Review E, 2007, 76(4): 046115.
[35]RODGERS J L, NICEWANDER W A. Thirteen ways to look at the correlation coefficient[J]. The American Statistician, 2012, 42(1): 59-66.
[36]SPEARMAN C. The proof and measurement of association between two things[J]. American Journal of Psychology, 1904, 15(1): 72-101.
[37]KENDALL M. A new measure of rank correlation[J]. Biometrika, 1938, 30: 81-93.
[38]BREESE J, HECHERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.San Francisco:Morgan Kaufmann, 1998:43-52.
[39]SWETS J A. Information retrieval systems[J]. Science, 1963, 141(3577):245-250.
[40]JOONSEOK L, MINGXUAN S, GUY L. A comparative study of collaborative filtering algorithms[J/OL].(2012-03-14)
[2016-01-04].https://arxiv.org/abs/1205.3193.
[41]刘建国, 周涛, 郭强, 等. 个性化推荐系统评价方法综述[J]. 复杂系统与复杂性科学, 2009, 6(3):1-10.
[42]应毅,刘亚军,陈诚.基于云计算的个性化推荐系统[J].计算机工程与应用,2015,51(13):111-117.
[43]GONG S. A collaborative filtering recommendation algorithm based on user clustering and item clustering[J]. Journal of Software, 2010, 5(7):745-752.
[44]涂丹丹,舒承椿,余海燕. 基于联合概率矩阵分解的上下文广告推荐算法[J]. 软件学报,2013,24(3):454-464.
[45]BAUER J, NANOPOULOS A. A framework for matrix factorization based on general distributions[C]∥Proceedings of the 8-th ACM Conference on Recommender Systems.Silicon Valley: ACM Press,2014: 249-256.