参考文献
[1] Guo G, Zhang J, Yorke-Smith N. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings[C]//Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2015: 123-129.
[2] Fernández-Tobías I, Braunhofer M, Elahi M, et al. Alleviating the New User Problem in Collaborative Filtering by Exploiting Personality Information[J]. User Modeling and User-Adapted Interaction, 2016, 26 (2): 221-255.
[3] 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.
[4] He R, Mcauley J. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering[C]//International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2016: 507-517.
[5] Silva E Q D, Camilo-Junior C G, Pascoal L M L, et al. An Evolutionary Approach for Combining Results of Recommender Systems Techniques Based on Collaborative Filtering[J]. Expert Systems with Applications, 2016, 53: 204-218.
[6] Zhang J, Lin Y, Lin M, et al. An Effective Collaborative Filtering Algorithm Based on User Preference Clustering[J]. Applied Intelligence, 2016, 45 (2): 1-11.
[7] Yang B, Lei Y, Liu J, et al. Social Collaborative Filtering by Trust. [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, PP (99): 2747-2753.
[8] Zheng X, Luo Y, Sun L, et al. A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques[J]. Chinese Journal of Electronics, 2016, 25 (2): 334-340.
[9] Kushwaha N, Sun X, Vyas O P, et al. SemPMF: Semantic Inclusion by Probabilistic Matrix Factorization for Recommender System[J]. 2016, 27 (2): 294-310.
[10] Qiu Y, Lin C J, Juan Y C, et al. Recosystem: Recommender System using Matrix Factorization[J]. 2015, 45 (3): 39-47.
[11] Nguyen G T, Ahn H. A Combining Method of Content-based Information into Matrix Factorization Recommendation System[J]. 2016, 53: 204-218.
[12] Pirasteh P, Hwang D, Jung J J. Exploiting Matrix Factorization to Asymmetric User Similarities in Recommendation Systems[J]. Knowledge-Based Systems, 2015, 83 (1): 51-57.
[13] 何佳知．基于内容和协同过滤的混合算法在推荐系统中的应用研究［D］．上海：东华大学，2016.
[14] 刘晓光．基于遗忘理论和加权二部图的推荐系统研究［D］．贵阳：贵州大学，2015.
[15] 王立才．上下文感知推荐系统若干关键技术研究［D］．北京：北京邮电大学，2012.
[16] 冷亚军，陆青，梁昌勇．协同过滤推荐技术综述［J］．模式识别与人工智能，2014，27（8）：720-734.
[17] 荣辉桂，火生旭，胡春华，等．基于用户相似度的协同过滤推荐算法［J］．通信学报，2014（2）：16-24.
[18] 朱夏，宋爱波，东方，等．云计算环境下基于协同过滤的个性化推荐机制［J］．计算机研究与发展，2014，51（10）：2255-2269.
[19] 洪舒怡．基于矩阵分解的推荐系统模型和算法改进研究［D］．厦门：厦门大学，2016.
[20] Gomez-Uribe C A, Hunt N. The Netflix Recommender System: Algorithms, Business Value, and Innovation[J]. Acm Transactions on Management Information Systems, 2016, 6 (4): 13.
[21] Rampure V, Tiwari A. A Rough Set Based Feature Selection on KDD CUP 99 Data Set[J]. International Journal of Database Theory & Application, 2015, 8.
[22] 赵恒．基于LBS的本地美食推荐系统的研究与实现［D］．成都：电子科技大学，2015.
[23] Matuszyk P, Vinagre J, Spiliopoulou M, et al. Forgetting Methods for Incremental Matrix Factorization in Recommender Systems[C]//ACM Symposium on Applied Computing. ACM, 2015: 947-953.
[24] Zhao C, Peng Q, Zhang Z. A Matrix Factorization Algorithm with Hybrid Implicit and Explicit Attributes for Recommender Systems[J]. Journal of Xian Jiaotong University, 2016.
[25] 陈克寒，韩盼盼，吴健．基于用户聚类的异构社交网络推荐算法［J］．计算机学报，2013，36（2）：349-359.
[26] 金淳，张一平．基于Agent的顾客行为及个性化推荐仿真模型［J］．系统工程理论与实践，2013，33（2）：463-472.
[27] Lin H, Yang X, Wang W, et al. A Performance Weighted Collaborative Filtering Algorithm for Personalized Radiology Education[J]. Journal of Biomedical Informatics, 2014, 51: 107.
[28] 乌达巴拉，汪增福．基于半监督的短语情感倾向性分析方法［J］．模式识别与人工智能，2016，29（4）：289-297.
[29] 张锋，常会友．使用BP神经网络缓解协同过滤推荐算法的稀疏性问题［J］．计算机研究与发展，2015，43（4）：667-672.
[30] Boutet A, Frey D, Guerraoui R, et al. Privacy-Preserving Distributed Collaborative Filtering[J]. Computing, 2016, 98 (8): 827-846.
[31] Liu J, Tang M, Zheng Z, et al. Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation[J]. IEEE Transactions on Services Computing, 2016: 1-1.
[32] Nasi R, Taber A, Vliet N V. Empty Forests, Empty Stomachs? Bushmeat and Livelihoods in the Congo and Amazon Basins[J]. International Forestry Review, 2016, 13 (3): 14.
[33] Sampoornam M. Collaborative Based Filtering Approach for Web Service Recommendations Using GEO Locations[J]. 2015, 3 (3): 1045-1047.
[34] Jr E C T, Ferrucci P, Duffy M. Facebook Use, Envy, and Depression Among college Students: Is Facebooking Depressing?[J]. Computers in Human Behavior, 2015, 43 (43): 139-146.
[35] Kingsbury B E D, Sainath T N, Sindhwani V. Low-rank Matrix Factorization for Deep Belief Network Training with High-dimensional Output targets[J]. 2016: 6655-6659.
[36] 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（3）：454-464.
[37] 刁海伦．基于社交网络的个性化推荐算法研究［D］．天津：天津师范大学，2015.
[38] Hernando A, Lazaro J, Gil E, et al. Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment. [J]. IEEE Journal of Biomedical & Health Informatics, 2016, 20 (4): 1016-1025.
[39] Shambour Q, Lu J. An Effective Recommender System by Unifying User and Item Trust Information for B2B Applications[J]. Journal of Computer & System Sciences, 2015, 81 (7): 1110-1126.
[40] Zheng X L, Chen C C, Hung J L, et al. A Hybrid Trust-Based Recommender System for Online Communities of Practice[J]. IEEE Transactions on Learning Technologies, 2015, 8 (4): 345-356.
[41] Golbeck J. Personalizing Applications through Integration of Inferred Trust Values in Semantic Web-based Social Networks[J]. Proceedings of Semantic Network Analysis Workshop, 2005.
[42] Wu Z, Yu X, Sun J. An Improved Trust Metric for Trust-Aware Recommender Systems[C]//International Workshop on Education Technology and Computer Science. IEEE, 2009: 947-951.
[43] 秦继伟，郑庆华，郑德立，等．结合评分和信任的协同推荐算法［J］．西安交通大学学报，2013，47（4）：100-104.
[44] 王海艳，张大印．一种可信的基于协同过滤的服务选择模型［J］．电子与信息学报，2013，35（2）：349-354.
[45] Zeng J, Gao M, Wen J, et al. A Hybrid Trust Degree Model in Social Network for Recommender System[C]//Iiai, International Conference on Advanced Applied Informatics. IEEE, 2014: 37-41.
[46] 朱强，孙玉强．一种基于信任度的协同过滤推荐方法［J］．清华大学学报（自然科学版），2014（3）：360-365.
[47] Ma T, Zhou J, Tang M, et al. Social Network and Tag Sources Based Augmenting Collaborative Recommender System[J]. Ieice Transactions on Information & Systems, 2015, E98. D (4): 902-910.
[48] Shen X, Long H, Ma C. Incorporating Trust Relationships in Collaborative Filtering Recommender System[C]//IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, NETWORKING and Parallel/distributed Computing. IEEE, 2015: 1-8.
[49] Parnes P, Synnes K, Schefström D. m-Tunnel: A Multicast Tunneling System with a User-based Quality-of-Service Model[J]. Springer, 2016, 1309: 87-96.
[50] Li D, Chen C, Lv Q, et al. An Algorithm for Efficient Privacy-Preserving Item-based Collaborative Filtering[J]. Future Generation Computer Systems, 2016, 55: 311-320.
[51] 潘骏驰，张兴明，汪欣．融合用户可信度的改进奇异值分解推荐算法［J］．小型微型计算机系统，2016，37（10）：2171-2176.
[52] Chen C, Zheng X, Zhu M, et al. Recommender System with Composite Social Trust Networks[J]. International Journal of Web Services Research, 2016, 13 (2): 56-73.
[53] Gohari F S, Haghighi H, Aliee F S. A Semantic-enhanced Trust based Recommender System Using Ant Colony Optimization[J]. Applied Intelligence, 2016: 1-37.
[54] Gillis N, Vavasis S A. Fast and Robust Recursive Algorithmsfor Separable Non-negative Matrix Factorization[J]. Pattern Analysis & Machine Intelligence IEEE Transactions on, 2014, 36 (4): 698-714.
[55] 张志绮．基于用户关系的矩阵分解推荐算法研究［D］．北京：北京交通大学，2016.
[56] 王升升，赵海燕，陈庆奎，等．基于社交标签和社交信任的概率矩阵分解推荐算法［J］．小型微型计算机系统，2016，37（5）：921-926.
[57] Song Q, Cheng J, Lu H. Incremental Matrix Factorization via Feature Space Re-learning for Recommender System[C]//The, ACM Conference, 2015: 277-280.
[58] Salakhutdinov R, Mnih A. Probabilistic Matrix Factorization. [J]. Advances in Neural Information Processing Systems, 2015: 1257-1264.
[59] Hernando A, Bobadilla J, Ortega F. A Non-negative Matrix Factorization for Collaborative Filtering Recommender Systems Based on a Bayesian Probabilistic Model[J]. Knowledge-Based Systems, 2016, 97 (C): 188-202.
[60] Lee H, Kwon J. Improvement of Matrix Factorization-based Recommender Systems Using Similar User Index[J]. International Journal of Software Engineering & Its Applications, 2015, 9.
[61] Boutet A, Frey D, Guerraoui R, et al. Privacy-preserving Distributed Collaborative Filtering[J]. Computing, 2016, 98 (8): 827-846.
[62] Yu M C, Wu Y C J, Alhalabi W, et al. Research Gate[J]. Computers in Human Behavior, 2016, 55 (PB): 1001-1006.
[63] 朱夏，宋爱波，东方，等．云计算环境下基于协同过滤的个性化推荐机制［J］．计算机研究与发展，2014，51（10）：2255-2269.
[64] 杜永萍，黄亮，何明．融合信任计算的协同过滤推荐方法［J］．模式识别与人工智能，2014，27（5）：417-425.
[65] 顾梁，杨鹏，罗军舟．一种播存网络环境下的UCL协同过滤推荐方法［J］．计算机研究与发展，2015，52（2）：475-486.
[66] 刘胜宗，廖志芳，吴言凤，等．一种融合用户评分可信度和相似度的协同过滤推荐算法［J］．小型微型计算机系统，2014，35（5）：973-977.
[67] 张佳，林耀进，林梦雷，等．基于目标用户近邻修正的协同过滤推荐算法［J］．模式识别与人工智能，2015，28（9）：802-810.
[68] 杨丽，钮心忻，黄玮．基于协同谱聚类的推荐系统托攻击防御算法［J］．北京邮电大学学报，2015，38（6）：81-86.
[69] 李贵，王爽，李征宇，等．基于时间加权三部图的分众分类标签推荐算法［J］．小型微型计算机系统，2016，37（2）：269-274.
[70] 高升，任思婷，郭军．基于潜在因子模型的跨领域信息推荐算法［J］．电信科学，2015，31（7）：75-79.
[71] Zeng J, Leng B, Xiong Z. 3-D Object Retrieval Using Topic Model[J]. Multimedia Tools and Applications, 2015, 74 (18): 7859-7881.
[72] Koren Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2008: 426-434.
[73] Koren Y. Collaborative Filtering with Temporal Dynamics[J]. Communications of the ACM, 2010, 53 (4): 89-97.
[74] Levy O, Goldberg Y. Neural Word Embedding as Implicit Matrix Factorization[J]. Advances in Neural Information Processing Systems, 2014, 3: 2177-2185.
[75] Lian D, Zhao C, Xie X, et al. Geo MF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014: 831-840.
(1) 注：本书中的矩阵、向量用斜体表示，而不用黑体表示。全书统一。