I. Barjasteh, R. Forsati, D. Ross, A. H. Esfahanian, and H. Radha, Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach, IEEE Transactions on Knowledge and Data Engineering, vol.28, issue.6, pp.1462-1474, 2016.
DOI : 10.1109/TKDE.2016.2522422

D. H. Alahmadi and X. J. Zeng, Twitter-Based Recommender System to Address Cold-Start: A Genetic Algorithm Based Trust Modelling and Probabilistic Sentiment Analysis, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp.1045-1052, 2015.
DOI : 10.1109/ICTAI.2015.149

A. L. Pereira and E. R. Hruschka, Simultaneous co-clustering and learning to address the cold start problem in recommender systems, Knowledge-Based Systems, vol.82, pp.11-19, 2015.
DOI : 10.1016/j.knosys.2015.02.016

A. Q. Macedo, L. B. Marinho, and R. L. Santos, Context-Aware Event Recommendation in Event-based Social Networks, Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, pp.123-130, 2015.
DOI : 10.1145/2792838.2800187

L. Quijano-sánchez, J. A. Recio-garcía, and B. Díaz-agudo, A reusable methodology for the instantiation of social recommender systems, IEEE 25th International Conf. on Tools with Artificial Intelligence (ICTAI), pp.775-782, 2013.

A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, Measurement and analysis of online social networks, Proceedings of the 7th ACM SIGCOMM conference on Internet measurement , IMC '07, pp.29-42, 2007.
DOI : 10.1145/1298306.1298311

R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, 2014.
DOI : 10.1017/CBO9781139088510

C. Z. Felício, K. V. Paixão, G. Alves, and S. De-amo, Social prefrec framework: leveraging recommender systems based on social information, Proc. 3rd Symposium on Knowledge Discovery, Mining and Learning, pp.66-73, 2015.

C. Z. Felício, K. V. Paixão, G. Alves, S. De-amo, and P. Preux, Exploiting social information in pairwise preference recommender system, Journal of Information and Data Management

C. Z. Felício, C. M. De-almeida, G. Alves, F. S. Pereira, K. V. Paixão et al., Advances in AI: 29th Canadian Conf, ch. Visual Perception Similarities to Improve the Quality of User Cold Start Recommendations, pp.96-101, 2016.

Y. Koren, R. Bell, and C. Volinsky, Matrix Factorization Techniques for Recommender Systems, Computer, vol.42, issue.8, pp.30-37, 2009.
DOI : 10.1109/MC.2009.263

M. Jamali and M. Ester, A matrix factorization technique with trust propagation for recommendation in social networks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.135-142, 2010.
DOI : 10.1145/1864708.1864736

G. Guo, J. Zhang, and N. Yorke-smith, A novel bayesian similarity measure for recommender systems, Proc. 23rd International Joint Conf. on Artificial Intelligence (IJCAI), pp.2619-2625, 2013.

P. Massa and P. Avesani, Trust-aware recommender systems, Proceedings of the 2007 ACM conference on Recommender systems , RecSys '07, pp.17-24, 2007.
DOI : 10.1145/1297231.1297235

E. Viriato-de-melo, E. A. Nogueira, and D. Guliato, Content-Based Filtering Enhanced by Human Visual Attention Applied to Clothing Recommendation, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp.644-651, 2015.
DOI : 10.1109/ICTAI.2015.98

C. Sammut and G. I. Webb, Encyclopedia of Machine Learning, ch. Leave-One-Out Cross-Validation, pp.600-601, 2010.
DOI : 10.1007/978-0-387-30164-8

H. Ma, H. Yang, M. R. Lyu, and I. King, SoRec, Proceeding of the 17th ACM conference on Information and knowledge mining, CIKM '08, pp.931-940, 2008.
DOI : 10.1145/1458082.1458205

B. Yang, Y. Lei, D. Liu, and J. Liu, Social Collaborative Filtering by Trust, Proc. Twenty-Third International Joint Conf. on Artificial Intelligence, ser. IJCAI, pp.2747-2753, 2013.
DOI : 10.1109/TPAMI.2016.2605085

G. Guo, J. Zhang, Z. Sun, and N. Yorke-smith, Librec: A java library for recommender systems, 23rd Conf. on User Modeling, Adaptation, and Personalization (UMAP), 2015.

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '02, pp.253-260, 2002.
DOI : 10.1145/564376.564421

L. H. Son, HU-FCF++: A novel hybrid method for the new user cold-start problem in recommender systems, Engineering Applications of Artificial Intelligence, vol.41, pp.207-222, 2015.
DOI : 10.1016/j.engappai.2015.02.003

B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades, Facing the cold start problem in recommender systems, Expert Systems with Applications, vol.41, issue.4, pp.2065-2073, 2014.
DOI : 10.1016/j.eswa.2013.09.005

I. Guy, Recommender Systems Handbook, Social Recommender Systems, pp.511-543, 2015.

H. Ma, T. C. Zhou, M. R. Lyu, and I. King, Improving Recommender Systems by Incorporating Social Contextual Information, ACM Transactions on Information Systems, vol.29, issue.2, pp.1-9, 2011.
DOI : 10.1145/1961209.1961212

W. Reafee, N. Salim, and A. Khan, The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems, PLOS ONE, vol.107, issue.10, pp.1-20, 2016.
DOI : 10.1371/journal.pone.0154848.s002