Modern Approaches to Building Recommender Systems for Online Stores

Lyudmila Onokoy; Jurijs Lavendels

doi:10.2478/acss-2019-0003

.blurhash-client-img { display: none !important; }

Modern Approaches to Building Recommender Systems for Online Stores

Applied Computer Systems

Volume 24 (2019): Issue 1 (May 2019)

By: Lyudmila Onokoy and Jurijs Lavendels

Open Access

|Jun 2019

Abstract

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.

References

[1] E-commerce trends. [Online] Available from: https://don16obqbay2c.cloudfront.net/wp-content/uploads/ru/Retina_E-commerce-trends_Part2-1480613113.png
Search in Google Scholar Back to article
[2] eMarketer. Top 10 US Ecommerce Companies in 2018. [Online] Available from: https://www.emarketer.com/content/top-10-usecommerce-companies-in-2018
Search in Google Scholar Back to article
[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.2005.9910.1109/TKDE.2005.99
Search in Google Scholar Back to article
[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-2012-22-2110.15514/ISPRAS-2012-22-21
Search in Google Scholar Back to article
[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/10.1145/282787210.1145/2827872
Search in Google Scholar Back to article
[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_810.1007/978-0-387-85820-3_8
Search in Google Scholar Back to article
[7] X. Su and T. M. Khoshgoftaar, “Survey of collaborative Filtering Techniques,” Advances in Artifical Intelligence, vol. 2009. https://doi.org/10.1155/2009/42142510.1155/2009/421425
Search in Google Scholar Back to article
[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.2009.26310.1109/MC.2009.263
Search in Google Scholar Back to article
[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/10.1137/1.9781611972757.4310.1137/1.9781611972757.43
Search in Google Scholar Back to article
[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/10.1145/1148170.114825710.1145/1148170.1148257
Search in Google Scholar Back to article
[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_410.1007/978-0-387-85820-3_4
Search in Google Scholar Back to article
[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/10.1145/1401890.140194410.1145/1401890.1401944
Search in Google Scholar Back to article
[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.2015.12.01810.1016/j.knosys.2015.12.018
Search in Google Scholar Back to article
[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/10.1145/1835804.183589510.1145/1835804.1835895
Search in Google Scholar Back to article
[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/10.1145/564418.56442110.1145/564418.564421
Search in Google Scholar Back to article
[16] S. Rendle, “Factorization machines,” in Proceedings of the 10th IEEE International Conference on Data Mining, IEEE, 2010. https://doi.org/10.1109/ICDM.2010.12710.1109/ICDM.2010.127
Search in Google Scholar Back to article
[17] Jannach D., Zanker M., Felfernig A., Friedrich G. Recommender Systems. An Introduction. New York: Cambridge University Press, 2011, 352 p.10.1017/CBO9780511763113
Search in Google Scholar Back to article
[18] P. Melville and V. Sindhwani, “Recommender Systems,” In: C. Sammut and G. Webb, Eds., Encyclopedia of Machine Learning, Springer, Berlin, 2010, pp. 829–838.10.1007/978-0-387-30164-8_705
Search in Google Scholar Back to article
[19] M. Tim Jones, “Ai Application Programming (Programming Series)”, 2nd edition, Charles River Media, 2005.
Search in Google Scholar Back to article
[20] L. Wang, L. Cheng, and G. Zhao, “Machine Learning for Human Motion Analysis: Theory and Practice,” IGI Global, 2009. https://doi.org/10.4018/978-1-60566-900-710.4018/978-1-60566-900-7
Search in Google Scholar Back to article
[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_1210.1007/978-3-540-72079-9_12
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/acss-2019-0003 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683

Journal RSS Feed

Language: English

Page range: 18 - 24

Published on: Jun 20, 2019

Published by: Riga Technical University

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Collaborative filtering,

combined co-filtering,

content filtering,

typical recommender system

Related subjects:

Computer sciences,

Artificial intelligence,

Information technology,

Project management,

Software development

© 2019 Lyudmila Onokoy, Jurijs Lavendels, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 24 (2019): Issue 1 (May 2019)