Design a Hybrid Recommender System Solving Cold-start Problem Using Clustering and Chaotic PSO Algorithm

[1] E. R. Núñez-Valdéz, J. M. C. Lovelle, O. S. Martínez, V. García-Díaz, P. O. de Pablos, & C. E. M. Marín, “Implicit feedback techniques on recommender systems applied to electronic books,” Computers in Human Behavior, vol. 28 ,no. 4, pp. 1186-1193, 2012. [2] S. Roy, & S. C. Guntuku, “Latent Factor Representations for Cold-Start Video Recommendation,” In Proceedings of the 10th ACM Conference on Recommender Systems, pp. 99-106, ACM, 2016. [3] A. B. Barragáns-Martínez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-López, F. A. Mikic-Fonte, & A. Peleteiro, “A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition,” Information Sciences, vol. 180, no. 22, pp. 4290-4311, 2010. [4] S. K. Lee, Y. H. Cho, & S. H. Kim, “Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations,” Information Sciences, vol. 180, no. 11, pp. 2142-2155, 2010. [5] J. E. S. U. S. Bobadilla, F. Serradilla, & A. Hernando, “Collaborative filtering adapted to recommender systems of e-learning,” Knowledge-Based Systems, vol. 22, no. 4, pp. 261-265, 2009 [6] J. J. Castro-Schez, R. Miguel, D. Vallejo, & L. M. López-López, “A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals,” Expert Systems with Applications, vol. 38, no. 3, pp. 2441-2454, 2011. [7] T. T. S. Nguyen, H. Y. Lu, & J. Lu, “Web-page recommendation based on web usage and domain knowledge,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2574-2587, 2014. [8] L. H. Son, “Dealing with the new user cold-start problem in recommender systems: A comparative review,” Information Systems, vol. 58, pp. 87-104, 2016. [9] C. C. Aggarwal, “Recommender Systems,” Springer, 2016. [10] L. Safoury, & A. Salah, “Exploiting user demographic attributes for solving cold-start problem in recommender system,” Lecture Notes on Software Engineering, vol. 1, no. 3, pp. 303, 2013. [11] F. Peng, J. Lu, Y. Wang, R. Yi-Da Xu, C. Ma, & J. Yang, “N-dimensional Markov random field prior for cold-start recommendation,” Neurocomputing, vol. 191, pp. 187-199, 2016. [12] C. B. Huang, & S. J. Gong, “Employing rough set theory to alleviate the sparsity issue in recommender system.” International Conference on Machine Learning and Cybernetics, Vol. 3, pp. 1610-1614, IEEE, 2008. [13] J. Xu, Y. Yao, H. Tong, X. Tao, & J. Lu, “Ice-breaking: Mitigating cold-start recommendation problem by rating comparison,” In Proceedings of the 24th International Conference on Artificial Intelligence, pp. 3981-3987, AAAI Press, 2015. [14] A. Hernando, J. Bobadilla, F. Ortega, & A. Gutiérrez, “A probabilistic model for recommending to new cold-start non-registered users,” Information Sciences, vol. 376, pp. 216-232, 2017. [15] S.Feil, M. Kretzer, K. Werder, & A. Maedche, “Using Gamification to Tackle the Cold-Start Problem in Recommender Systems,” In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, pp. 253-256, ACM, 2016. [16] L. H. Son, “HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems,” Expert Systems with Applications: An International Journal, vol. 41, no. 15, pp. 6861-6870, 2014. [17] 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. [18] M. Aharon, O. Anava, N. Avigdor-Elgrabli, D. Drachsler-Cohen, S. Golan, & O. Somekh, “ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations,” In Proceedings of the 9th ACM Conference on Recommender Systems, pp. 83-90, ACM, 2015. [19] I. Fernández-Tobías, M. Braunhofer, M. Elahi, F. Ricci, & Cantador, “I Alleviating the new user problem in collaborative filtering by exploiting personality information,” User Modeling and User-Adapted Interaction, pp. 1-35, 2015. [20] I. Barjasteh, R. Forsati, F. Masrour, A. H. Esfahanian, & H. Radha, “Cold-start item and user recommendation with decoupled completion and transduction,” In Proceedings of the 9th ACM Conference on Recommender Systems, pp. 91-98, ACM, 2015. [21] A. L. V. Pereira, & 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. [22] GroupLens Research, MovieLens, 2014, Available at: http://grouplens.org/datasets/ movielens/. [23] H. J. Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Information Sciences, vol. 178, no. 1, pp. 37-51, 2008. [24] B. Alatas, E. Akin, & A. B. Ozer, “Chaos embedded particle swarm optimization algorithms,” Chaos, Solitons & Fractals, vol. 40, no. 4, pp. 1715-1734, 2009. [25] P. J. Rousseeuw, ‘‘Silhouettes: A graphical aid to the interpretation and validation of cluster analysis’’ Journal of computational and applied mathematics, vol. 20, pp. 53-65, 1987.