参考文献

[1]　孟祥武，刘树栋，张玉洁．社会化推荐系统研究［J］．软件学报，2015，26（6）：1356-1372.

[2]　王国霞，刘贺平，李擎．基于万有引力的个性化推荐算法［J］．工程科学学报，2015（2）：255-259.

[3] Adibi P, Ladani BT. A collaborative Filtering Recommender System-based on User’s Time Pattern Activity[C]//Information and Knowledge Technology (IKT) , 2013 5th Conference on IEEE, Kharagpur, 2013: 252-257.

[4]　Jia C X, Liu R R. Improve the Algorithmic Performance of Collaborative Filtering by Using the Interevent Time Distribution of Human Behaviors[J]. Physica A Statistical Mechanics & Its Applications, 2015, 436: 236-245.

[5]　 P, Grzegorzewski P.Vague preferences in recommender systems［J］.Expert Systems with Applications, 2015，42（24）：9402-9411.

[6]　郑志高，刘京，王平．时间加权不确定近邻协同过滤推荐算法［J］．计算机科学，2014，41（8）：7-12.

[7]　李源鑫，肖如良，陈洪涛．时间衰减制导的协同过滤相似度计算［J］．计算机系统应用，2013，22（11）：129-134.

[8]　刘东辉，彭德巍，张晖．一种基于时间加权和用户特征的协同过滤推荐算法［J］．武汉理工大学学报，2012，34（05）：144-148.

[9]　Chen C, Yin H, Yao J, et al. TeRec: A Temporal Recommender System over Tweet Stream[J]. Proceedings of the Vldb Endowment, 2013, 6 (12): 1254-1257.

[10]　Huang Y, Cui B, Zhang W, et al. TencentRec: Real-time Stream Recommendation in Practice[C]//ACM SIGMOD International Conference on Management of Data. ACM, Melbourne, 2015: 227-238.

[11] Yin H, Cui B, Lu H, et al. A Unified Model for Stable and Temporal Topic Detection from Social Media Data[C]//2013 IEEE 29th International Conference on Data Engineering (ICDE) . IEEE Computer Society, Brisbane, 2013: 661-672.

[12] Yin H, Bin C, Ling C, et al. A Temporal Context-aware Model for User Behavior Modeling in Social Media Systems[C]//Association for Computing Machinery. Special Interest Group on Management of Data. International Conference Proceedings. Association for Computing Machinery, Salt Lake City, 2014: 1543-1554.

[13] Ren Y, Li G, Zhou W. Learning User Preference Patterns for Top-N Recommendations[C]//Web Intelligence and Intelligent Agent Technology (WI-IAT) , 2012 IEEE/WIC/ACM International Conferences on. IEEE, Hong Kong, 2012: 137-144.

[14] 吴毅涛，张兴明，王兴茂．基于用户模糊相似度的协同过滤推荐算法［J］．通信学报，2016，37（1）：198-206.

[15] 荣辉桂，火生旭，胡春华．基于用户相似度的协同过滤推荐算法［J］．通信学报，2014，35（2）：16-24.

[16] 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.

[17] 焦东俊．基于用户人口统计与专家信任的协同过滤算法［J］．计算机工程与科学，2015（01）：179-183.

[18] 王瑞琴，蒋云良，李一啸，等．一种基于多元社交信任的协同过滤推荐算法［J］．计算机研究与发展，2016，53（6）：1389-1399.

[19] Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009, 42 (8): 30-37.

[20] Paterek A. Improving Regularized Singular Value Decomposition for Collaborative Filtering[C]. //Preceedings of KDD Cup and Workshop, California, 2007, 39-42.

[21] WengJ, Miao C, Goh A. Improving Collaborative Filtering with Trust-based Metrics. [J]. Sac Proceedings of the Acm Symposium on Applied Computing, 2006: 1860-1864.

[22] Moradi P, Ahmadian S. A Reliability-based Recommendation Method to Improve trust-aware Recommender Systems[J]. Expert Systems with Applications, 2015, 42 (21): 7386-7398.

[23] Ghazanfar M A, Prugel-Bennett A. The Advantage of Careful Imputation Sources in Sparse Data-Environment of Recommender Systems: Generating Improved SVD-based Recommendations[J]. Informatics, 2013, 37 (1): 61-92.

[24] Scott D, Dumais S T, Furnas G W, et al. Indexing by latent Semantic Analysis[J]. Journal of the American Society for Information Science, 1990, 41 (6): 391-407.

[25] 夏小伍，王卫平．基于信任模型的协同过滤推荐算法［J］．计算机工程，2011，21期（21）：26-28.

[26] Chin W S, Yuan B W, Yang M Y, et al. LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems[J]. Technical report, 2015: 32-37.

[27] 邓仙荣．基于梯度提升回归算法的O2O推荐模型研究［D］．合肥：安徽理工大学，2016.