1. SISN
  2. All Volumes and Issues
  3. Volume 15, 2024
  4. Recommendation Systems in E-Commerce Applications

Recommendation Systems in E-Commerce Applications

SISN.
2024;
Volume 15
: pp. 252 - 259
https://doi.org/10.23939/sisn2024.15.252
Authors:
  1. Oleh Basystiuk
  2. Zoriana Rybchak
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Lviv, Ukraine

Nowadays, there are more and more web applications of all kinds. Each of them solves a specific problem and makes life easier for its users. Web applications come in many different types: from a platform for learning courses and watching movies to an online store selling goods. The best systems are those that make things as easy as possible for the user, behave like old friends who know the behavior and tastes of their users and can predict their next move. It would be useful to integrate such system behavior into an online store system, as nowadays, a huge number of people prefer to buy goods online, saving time and effort. Thus, recommender systems have become an important tool for improving the efficiency of e-commerce stores and ensuring customer satisfaction. This study analyzes the main approaches to the application of recommender systems for online stores, substantiates the advantages and feasibility of the selected technologies for the implementation of an online store information system using neural networks.

recommendation systems
information technology
e-commerce
  1. Kulkarni, A., Shivananda, A., Krishnan, V. A. (2022). Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques, pp. 264, Publisher Apress.
  2. Izonin, I., Tkachenko, R., Vitynskyi, P., Zub, K., Tkachenko, P., Dronyuk, I. (2020). Stacking-based GRNN-SGTM Ensemble Model for Prediction Tasks.  International Conference on Decision  Aid  Sciences and Application (DASA), 326–330.
  3. Shakhovska, N., Basystiuk, O., & Shakhovska, K. (2019). Development of the Speech-to-Text Chatbot Interface Based on Google API. In MoMLeT, 212–221.
  4. Falk, K. (2019). Practical Recommender Systems. Manning Publications. https://www.perlego.com/book/1469487/practical-recommender-systems-pdf
  5. Genovese, A. (2020, October 6). Recommendation algorithms in e-commerce industry. Alexgenovese. https://alexgenovese.it/blog/recommendation-algorithms-in-e-commerce-ind...
  6. Masolo, C. (2023, January 18). EBay New Recommendations Model with Three Billion Item Titles. Infoq.      https://www.infoq.com/news/2023/01/ebay-recommendations-odel/
  7. Krysik, A. (2021, October 14). Amazon's Product Recommendation System In 2021: How Does The Algorithm Of The eCommerce  Giant  Work? Recostream. https://recostream.com/blog/amazon-recommendation- system
  8. Faggella, D. (2022, May 10). Artificial  Intelligence  at Alibaba  – Two Current Use-Cases. Emerj. https://emerj.com/ai-sector-overviews/artificial-intelligence-at-alibaba/
  9. Marr, B. (2022, May 10). The Amazing Ways Retail Giant Zalando Is Using Artificial Intelligence. Bernard Marr & Co. https://bernardmarr.com/the-amazing-ways-retail-giant-zalando-is-using-a...
  10. Rybchak, Z., & Basystiuk, O. (2017). Analysis of methods and means of text mining. ECONTECHMOD. AN INTERNATIONAL QUARTERLY JOURNAL., 6(2), 73–78.
  11. Shakhovska, N., Vovk, O., Kryvenchuk, Y. ((2018). Uncertainty reduction in Big data catalogue for information product quality evaluation. Eastern-European Journal of Enterprise Technologies., 1(2), 12–20.
  12. Basystiuk, O., Melnykova, N. (2022). Multimodal Approaches for Natural Language Processing in Medical Data. 5th International Conference on Informatics & Data-Driven Medicine, 246–252.
  13. Havryliuk, M., Dumyn, I., Vovk, O. (2023).  Extraction of  Structural Elements of  the Text Using Pragmatic Features for the Nomenclature of Cases Verification. Advances in Intelligent Systems, Computer Science and Digital Economics IV. CSDEIS 2022. Lecture Notes on Data Engineering and Communications Technologies., 158.    https://doi.org/10.1007/978-3-031-24475-9_57
  14. Shakhovska, N., Zherebetskyi, O., Lupenko, S. Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis. Applied Sciences. 2024; 14(5):1920. https://doi.org/10.3390/app14051920
  15. Basystiuk, O., Shakhovska, N., Bilynska, V., Syvokon, O., Shamuratov, O., & Kuchkovskiy, V. (2021). The Developing of the System for Automatic Audio to Text Conversion. Symposium on Information Technologies & Applied Sciences.
  16. Chukhray, N., Mrykhina, O., Izonin, I. Holistic Approach to R&D Products’ Evaluation for Commercialization under Open Innovations. Journal of Open Innovation: Technology, Market, and Complexity. 2022; 8(1):9. https://doi.org/10.3390/joitmc8010009
  17. Boyko, N., Mochurad, L., Parpan, U., & Basystiuk, O. (2021). Usage of Machine-based Translation Methods for Analyzing Open Data in Legal Cases. Cyber Hygiene and Conflict Management in Global Information Networks (CyberConf 2019), 328–338.
  18. Gunawardana, A., Shani, G. (2015). Evaluating Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_8
  19. Zheliznyak, I., Rybchak, Z., Zavuschak, I. (2017). Analysis of clustering algorithms. Advances in Intelligent Systems and Computing, 305–314.
  20. Havryliuk, M., Kaminskyy, R., Yemets, K., Lisovych, T. (2023). Interactive Information System for Automated Identification of Operator Personnel by Schulte Tables Based on Individual Time Series. Advances in Artificial Systems for Logistics Engineering III. ICAILE 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-031-36115-9_34