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Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749 Li Z, Zhao H, Liu Q, Huang Z, Mei T, Chen E (2018) Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In: KDD, pp 1734–1743. ACM Candillier L, Meyer F, Boullé M (2007) Comparing state-of-the-art collaborative filtering systems. Lect Notes Comput Sci 4571:548 Jiang L, Cheng Y, Li Y, Li J, Yan H, Wang X (2018) A trust-based collaborative filtering algorithm for E-commerce recommendation system. J Ambient Intell Humaniz Comput 0(0):0 Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132 Koohi H, Kiani K, Hwangbo H, Kim Y (2017) A new method to find neighbor users that improves the performance of Collaborative Filtering. Expert Syst Appl 89:254–265 Zhang J, Lin Y, Lin M, Liu J (2016) An effective collaborative filtering algorithm based on user preference clustering. Appl Intell 45(2):230–240 Dakhel AM, Malazi HT, Mahdavi M (2018) A social recommender system using item asymmetric correlation. Appl Intell 48(3):527–540 Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Comput 42(8):30–37 Li Y, Wang D, He H, Jiao L, Xue Y (2017) Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems. Neurocomputing 249:48–63 Zhang F, Lu Y, Chen J, Liu S, Ling Z (2017) Robust collaborative filtering based on non-negative matrix factorization and R1-norm. Knowl-Based Syst 118:177–190 Hernando A, Bobadilla J, Ortega F (2016) A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl-Based Syst 97:188–202 Himabindu TVR, Padmanabhan V, Pujari AK (2018) Conformal matrix factorization based recommender system. Information Sciences Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS’07, pp 1257–1264, USA. Curran Associates Inc Pan W, Yang Q (2013) Transfer learning in heterogeneous collaborative filtering domains. Artif Intell. 197:39–55 Pan W (2016) A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177:447–453 Xin X, Liu Z, Lin C-Y, Huang H, Wei X, Guo P (2015) Cross-domain collaborative filtering with review text. In: Proceedings of the 24th international conference on artificial intelligence, IJCAI’15, pp 1827–1833. AAAI Press Guo G, Qiu H, Tan Z, Liu Y, Ma J, Wang X (2017) Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowl-Based Syst 138:202–207 Li B, Yang Q, Xue X (2009) Can movies and books collaborate?: Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21st International Jont conference on artifical intelligence, IJCAI’09, pp 2052–2057, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc Khan MM, Ibrahim R, Ghani I (2017) Cross domain recommender systems: a systematic literature review. ACM Comput Surv 50(3):1–34 Zhu F, Wang Y, Chen C, Liu G, Orgun M, Wu J (2017) A Deep Framework for Cross-Domain and Cross-System Recommendations. pp 3711–3717 He M, Zhang J, Yang P, Yao K (2018) Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In: Proceedings of the 11th ACM international conference on web search and data mining - WSDM ’18, pp 225– 233 Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359 Cremonesi P, Tripodi A, Turrin R (2011) Cross-domain recommender systems. In: ICDMW2011: IEEE 11th international conference on data mining workshops, pp 496–503 Shi Y, Larson M, Hanjalic A (2011) Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: Proceedings of the 19th international conference on user modeling, adaption, and personalization, UMAP’11. Springer-Verlag, Berlin, pp 305–316 Enrich M, Braunhofer M, Ricci F (2013) Cold-start management with cross-domain collaborative filtering and tags. Springer, Berlin, pp 101–112 Fernández-Tobí (2014) Exploiting social tags in matrix factorization models for cross-domain collaborative filtering. In: CBREcsys@ recsys, pp 34–41 Sahu AK, Dwivedi P, Kant V (2018) Tags and item features as a bridge for cross-domain recommender systems. Procedia Comput Sci 125:624–631 Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer-Verlag New York, Inc., Secaucus Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl-Based Syst 100:175–187 Ma H, Yang H, Lyu MR, Sorec IK (2008) Social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, CIKM ’08, pp 931–940, New York, NY, USA. ACM Yu X, Chu Y, Jiang F, Guo Y, Gong D (2018) Knowledge-based systems SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl-Based Syst 141:80–91 Zheng X, Luo Y, Sun L, Ding X, Ji Z (2018) A novel social network hybrid recommender system based on hypergraph topologic structure. World Wide Web 21(4):985–1013 Chou S-Y, Yang Y-H, Jang J-SR, Lin Y-C (2016) Addressing cold start for next-song recommendation. In: Proceedings of the 10th ACM conference on recommender systems - RecSys ’16, pp 115–118 Valdéz ERN, Lovelle JMC, Martínez SO, García-díaz V, Ordoñez de Pablos P, Marín CEM (2012) Implicit feedback techniques on recommender systems applied to electronic books. Comput Hum Behav 28 (4):1186–1193 Crespo RG, Martínez OS, Lovelle JMC, García-Bustelo CPB, Gayo JEL, Ordoñez de Pablos P (2011) Recommendation system based on user interaction data applied to intelligent electronic books. Comput Hum Behav 27(4):1445–1449 Dang Thanh N, Son LH, Ali M (2017) Neutrosophic recommender system for medical diagnosis based on algebraic similarity measure and clustering. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–6. https://doi.org/10.1109/FUZZ-IEEE.2017.8015387 Le HS, Thong NT (2015) Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl-based Syst 74:133–150 Dwivedi P, Bharadwaj KK (2015) E-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst 32(2):264–276 Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56(Supplement C):156–166 Son LH (2015) HU-FCF++: a novel hybrid method for the new user cold-start problem in recommender systems. Eng Appl Artif Intell 41:207–222 Biswas S, Lakshmanan LVS, Roy SB (2017) Combating the cold start user problem in model based collaborative filtering. CoRR, arXiv:1703.00397 Son LH (2016) Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf Syst 58:87–104 Fernández-Tobías I, Cantador I, Kaminskas M, Ricci F (2012) Cross-domain recommender systems: a survey of the state of the art. In: Spanish conference on information retrieval Winoto P, Tang T (2008) If you like the devil wears prada the book, will you also enjoy the devil wears prada the movie? a study of cross-domain recommendations. N Gener Comput 26(3):209–225 Berkovsky S, Kuflik T, Ricci F (2007) Cross-Domain mediation in collaborative filtering 2 Cross-Domain mediation in collaborative filtering. User Model 4511:355–359 Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd international conference on World Wide Web - WWW ’13, pp 595–606 Pan W, Xiang EW, Liu NN, Yang Q (2010) Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of the 24th AAAI conference on artificial intelligence, AAAI’10, pp 230–235. AAAI Press Fang Z, Gao S, Li B, Li J, Liao J (2016) Cross-domain recommendation via tag matrix transfer. In: Proceedings - 15th IEEE international conference on data mining workshop, ICDMW 2015, pp 1235–1240 Zhao L, Pan SJ, Yang Q (2017) A unified framework of active transfer learning for cross-system recommendation. Artif Intell 245:38–55 Koren Y, Bell R (2015) Advances in collaborative filtering. Recommender systems handbook, 2nd edn. pp 77–118 Li T, Ding C (2006) The relationships among various nonnegative matrix factorization methods for clustering. In: Proceedings - IEEE international conference on data mining, ICDM, (1):362–371 Al-Shamri MYH, Bharadwaj KK (2008) Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst Appl 35(3):1386–1399 Huang J, Zhu K, Zhong N (2016) A probabilistic inference model for recommender systems. Appl Intell 45(3):686–694