References
- [1] E-commerce trends. [Online] Available from: https://don16obqbay2c.cloudfront.net/wp-content/uploads/ru/Retina_E-commerce-trends_Part2-1480613113.png
- [2] eMarketer. Top 10 US Ecommerce Companies in 2018. [Online] Available from: https://www.emarketer.com/content/top-10-usecommerce-companies-in-2018
- [3] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005. https://doi.org/10.1109/TKDE.2005.99
- [4] A. G. Gomzin, A. V. Korshunov, “Recommender systems: a survey of modern approaches,” Proceedings of the Institute for System Programming, vol. 22, 2012, pp. 401–418. https://doi.org/10.15514/ISPRAS-2012-22-21
- [5] F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 5, no. 4, 2016. https://doi.org/10.1145/2827872
- [6] G. Shani and A. Gunawardana, “Evaluating recommendation systems”, Recommender systems handbook. Springer, 2011. pp. 257–297. https://doi.org/10.1007/978-0-387-85820-3_8
- [7] X. Su and T. M. Khoshgoftaar, “Survey of collaborative Filtering Techniques,” Advances in Artifical Intelligence, vol. 2009. https://doi.org/10.1155/2009/421425
- [8] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009. https://doi.org/10.1109/MC.2009.263
- [9] D. Lemire and A. Maclachlan, “Slope one predictors for online rating-based collaborative filtering,” Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471–475, 2005. https://doi.org/10.1137/1.9781611972757.43
- [10] J. Wang, A. P. de Vries, and M. J. T. Reinders, “Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 501–508, 2006. https://doi.org/10.1145/1148170.1148257
- [11] C. Desrosiers and G. Karypis, A comprehensive survey of neighborhoodbased recommendation methods. Recommender systems handbook, F. Ricci et al. (eds), pp. 107–144. Springer, 2011. https://doi.org/10.1007/978-0-387-85820-3_4
- [12] Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434, ACM, 2008. https://doi.org/10.1145/1401890.1401944
- [13] A. Hernando, J. Bobadilla, and F. Ortega, “A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model,” Knowledge-Based Systems, vol. 97, pp. 188–202, 2016. https://doi.org/10.1016/j.knosys.2015.12.018
- [14] H. Steck, “Training and testing of recommender systems on data missing not at random,” in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 713–722, ACM, 2010. https://doi.org/10.1145/1835804.1835895
- [15] A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” in Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253–260, ACM, 2002. https://doi.org/10.1145/564418.564421
- [16] S. Rendle, “Factorization machines,” in Proceedings of the 10th IEEE International Conference on Data Mining, IEEE, 2010. https://doi.org/10.1109/ICDM.2010.127
- [17] Jannach D., Zanker M., Felfernig A., Friedrich G. Recommender Systems. An Introduction. New York: Cambridge University Press, 2011, 352 p.
- [18] P. Melville and V. Sindhwani, “Recommender Systems,” In: C. Sammut and G. Webb, Eds., Encyclopedia of Machine Learning, Springer, Berlin, 2010, pp. 829–838.
- [19] M. Tim Jones, “Ai Application Programming (Programming Series)”, 2nd edition, Charles River Media, 2005.
- [20] L. Wang, L. Cheng, and G. Zhao, “Machine Learning for Human Motion Analysis: Theory and Practice,” IGI Global, 2009. https://doi.org/10.4018/978-1-60566-900-7
- [21] R. Burke, “Hybrid web recommender systems,” The adaptive web, Lecture Notes in Computer Science, Springer, pp. 377–408, 2007. https://doi.org/10.1007/978-3-540-72079-9_12