The article presents current approaches to solving the problem of building recommender systems designed to intellectualize the user interface of online stores. Much attention is paid to modern methods of building recommender systems, analysing their strengths and weaknesses. Of greatest interest are the criteria for selecting effective methods for specific online stores and the authors’ concept of a typical recommender system of electronic commerce.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 E-commerce trends. [Online] Available from: https://don16obqbay2c.cloudfront.net/wp-content/uploads/ru/Retina_E-commerce-trends_Part2-1480613113.png
 eMarketer. Top 10 US Ecommerce Companies in 2018. [Online] Available from: https://www.emarketer.com/content/top-10-usecommerce-companies-in-2018
 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
 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
 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
 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
 X. Su and T. M. Khoshgoftaar “Survey of collaborative Filtering Techniques” Advances in Artifical Intelligence vol. 2009. https://doi.org/10.1155/2009/421425
 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
 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
 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
 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
 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
 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
 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
 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
 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
 Jannach D. Zanker M. Felfernig A. Friedrich G. Recommender Systems. An Introduction. New York: Cambridge University Press 2011 352 p.
 P. Melville and V. Sindhwani “Recommender Systems” In: C. Sammut and G. Webb Eds. Encyclopedia of Machine Learning Springer Berlin 2010 pp. 829–838.
 M. Tim Jones “Ai Application Programming (Programming Series)” 2nd edition Charles River Media 2005.
 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
 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