[1] LOPS P, de GEMMIS M, SEMERARO G, et al. Content-based and collaborative techniques for tag recommendation:an empirical evaluation[J]. Journal of Intelligent Information Systems, 2013, 40(1):41-61. [2] FENG S, CAO J, WANG J, et al. Recommendations based on comprehensively exploiting the latent factors hidden in items' ratings and content[J]. ACM Transactions on Knowledge Discovery from Data, 2017, 11(3):Article No. 35. [3] GEORGIOU T, EL ABBADI A, YAN X. Extracting topics with focused communities for social content recommendation[C]//Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. New York:ACM, 2017:1432-1443. [4] ALEXANDRIDIS G, SIOLAS G, STAFYLOPATIS A. Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models[J]. Data Mining & Knowledge Discovery, 2017, 31(4):1031-1059. [5] WANG J, DE VRIES A P, REINDERS M J T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]//SIGIR 2006:Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2006:501-508. [6] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//WWW'01:Proceedings of the 10th International Conference on World Wide Web. New York:ACM, 2001:285-295. [7] JAMALI M, ESTER M. Trustwalker:a random walk model for combining trust-based and item-based recommendation[C]//KDD'09:Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2009:397-406. [8] YILDIRIM H, KRISHNAMOORTHY M S. A random walk method for alleviating the sparsity problem in collaborative filtering[C]//RecSys'08:Proceedings of the 2008 ACM Conference on Recommender Systems. New York:ACM, 2008:131-138. [9] YIN F. Sparsity-tolerated algorithm with missing value recovering in user-based collaborative filtering recommendation[J]. Journal of Information & Computational Science, 2013, 10(15):4939-4948. [10] REN X, SONG M, HAIHONG E, et al. Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation[J]. Neurocomputing, 2017, 241:38-55. [11] SARWAR B M, KARYPIS G, KONSTAN J A, et al. Application of dimensionality reduction in recommender system-a case study[C]//Proceedings of the 2000 ACM WebKDD Web Mining for E-Commerce Workshop. New York:ACM, 2000:82-90. [12] 柴华,刘建毅.一种改进的Slope One推荐算法研究[J].信息网络安全,2015(2):77-81. (CHAI H, LIU J Y. Research on improved Slope One recommendation algorithm[J]. Netinfo Security, 2015(2):77-81.) [13] 孙丽梅,李晶皎,孙焕良.基于动态k近邻的Slope One协同过滤推荐算法[J].计算机科学与探索,2011,5(9):857-864. (SUN L M, LI J J, SUN H L. Slope One collaborative filtering recommendation algorithm based on dynamic k-nearest-neighborhood[J]. Journal of Frontiers of Computer Science and Technology, 2011, 5(9):857-864.) [14] LEMIRE D, MACLACHLAN A. Slope One predictors for online rating-based collaborative filtering[C]//SDM'05:Proceedings of the 2005 SIAM Data Mining Conference. Philadelphia, PA:Society for Industrial and Applied Mathematics (SIAM), 2005:471-475. [15] JANNACH D, ZANKER M, FELFERNIG A, et al.推荐系统[M].蒋凡,译.北京:人民邮电出版社,2013:26-28. (JANNACH D, ZANKER M, FELFERNIG A, et al. Recommender Systems[M]. JIANG F, translated. Beijing:Posts & Telecom Press, 2013:26-28.) [16] 董丽,邢春晓,王克宏.基于不同数据集的协作过滤算法评测[J].清华大学学报(自然科学版),2009,49(4):590-594. (DONG L, XING C X, WANG K H. Collaborative filtering algorithm evaluation for various datasets[J]. Journal of Tsinghua University (Science and Technology), 2009, 49(4):590-594.) [17] LIU Y, LIU D, XIE H, et al. A research on the improved Slope One algorithm for collaborative filtering[J]. International Journal of Computing Science & Mathematics, 2016, 7(3):245-253. [18] LI J, SUN L, WANG J. A Slope One collaborative filtering recommendation algorithm using uncertain neighbors optimizing[C]//WAIM 2011:Proceedings of the 2011 International Conference on Web-Age Information Management, LNCS 7142. Berlin:Springer, 2011:160-166. [19] TIAN S, OU L. An improved Slope One algorithm combining KNN method weighted by user similarity[C]//WAIM 2016:Proceedings of the 2016 International Conference on Web-Age Information Management, LNCS 9998. Cham:Springer, 2016:88-98. [20] 王潘潘,钱谦,王锋.改进加权Slope One协同过滤推荐算法研究[J].传感器与微系统,2017,36(7):138-141. (WANG P P, QIAN Q, WANG F. Study on improved weighted Slope one collaborative filtering algorithm[J]. Transducer and Microsystem Technologies, 2017, 36(7):138-141.) [21] VADIVELOU G, ILAVARASAN E. Fusion of Pearson similarity and Slope One methods for QoS prediction for Web services[C]//IC3I 2015:Proceedings of the 2014 International Conference on Contemporary Computing and Informatics. Piscataway, NJ:IEEE, 2014:1118-1124. [22] MI Z, XU C. A recommendation algorithm combining clustering method and Slope One scheme[C]//ICIC 2011:Proceedings of the 2011 Bio-Inspired Computing and Applications-International Conference on Intelligent Computing, LNCS 6840. Berlin:Springer, 2011:160-167. [23] LIANG T, FAN J, ZHAO J, et al. Improved Slope One collaborative filtering predictor using fuzzy clustering[C]//ADMA 2013:Proceedings of the 2013 International Conference on Advanced Data Mining and Applications, LNCS 8346. Berlin:Springer, 2013:181-192. |