Information system of feedback monitoring in social networks for the formation of recommendations for the purchase of goods

: pp. 218 - 234
Lviv Polytechnic National University, Information Systems and Networks Department
Lviv Polytechnic National University, Information Systems and Networks Department

This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.

The purpose of the work, object and subject of research, scientific novelty and practical significance of the work are formulated. An analysis of the peculiarities of the studied subject area and known means of solving the problem was carried out. Systems used in online marketing were used as a prototype system for generating recommendations based on feedback analysis. Comparative characteristics of the system with analogues were conducted and it was determined that the system is unique, and its development is relevant, since known existing similar systems do not recommend products to users based on the feedback of other users.

The general goal of system development is determined, the purpose, place of application of the system, development and implementation of the system are described. The criteria that are put forward when defining the goals are defined. Using the method of analysis of hierarchies, it was determined that the type of product being developed is a decision support system.

A conceptual model of the system has been developed. Project requirements are modeled – business requirements, user requirements, functional requirements, non-functional requirements. Input and output data of the system are defined.

The decision-making system is based on an algorithm for sentiment analysis of social networks users using the logistic regression method. Logistic regression is one of the most common machine learning algorithms that is easy to implement for classifying sets of linearly separable clusters of data. It quickly learns on large data sets and guarantees reliable results.

Economic, functional, financial and time effects should be expected from the implementation of such a recommendation system.

  1. Karagiannakos S. (2021). An introduction to Recommendation Systems: an overview of machine and deep learning architectures. AI Summer.
  2. Kumar P. P., Vairachilai S., Sirisha P., Mohanty S. N. (2021). Recommender Systems: Algorithms and Applications. Boca Raton, London, New York: CRC Press.
  3. Lobur M. V., Shvarts M. E., Stekh Y. V. (2018). Models and methods of forecasting recommendations for collaborative recommender systems (in Ukrainian). Bulletin of the Lviv Polytechnic National University. Series: Information systems and networks. 901, 68–75. feb/15581/181912maket-68-75.pdf.
  4. Meleshko E. V. (2018). Problems of modern recommender systems and methods of their solution (in Ukrainian). Control, navigation and communication systems: collection of scientific papers. Poltava: Poltava National Technical University, 4(50), 120–124. DOI:
  5. Cherednichenko O. Yu., Yangolenko O. V., Ivashchenko O. V., Matveev O. M. (2020). Models of recommendation formation in intelligent e-commerce systems (in Ukrainian). National Technical University “Kharkiv Polytechnic Institute”. Information processing systems, 1 (160), 32–39. DOI: 10.30748/soi.2020.160.04.
  6. Berko A. Yu., Vysotska V. V., Pasichnyk V. V. (2009). Systems of electronic content commerce: a monograph (in Ukrainian). Lviv: Publishing House of the Lviv Polytechnic National University.
  7. Vysotska V. (2018). Technologies of electronic commerce and Internet marketing: monograph (in Ukrainian). LAP LAMBERT Academic Publishing.
  8. Kraus K., Kraus M., Manzhura O. (2021). Electronic commerce and Internet trade: educational and methodological guide (in Ukrainian). Kyiv: Agrar Media Group.
  9. Aakanksha S., Sinha G. R., Bhatia S. (2021). New Opportunities for Sentiment Analysis and Information Processing. Advances in Data Mining and Database Management. IGI Global.
  10. Gasko R., Vysotska V., Chyrun L. (2017). Peculiarities of content analysis of user Internet activity for the formation of a section of the psychological state of the individual (in Ukrainian). Bulletin of the Lviv Polytechnic National University. Series: Computer Science and Information Technology, 864 (1), 221–238.
  11. Mashalkar A. (2020). Sentiment Analysis using Logistic Regression and Naive Bayes.
  12. Hu D. (2020). How to use logistic regression to perform sentiment analysis. pulse/how-use-logistic-regression-perform-sentiment-analysis-hu/.
  13. Soshnikova L.A. (2021). Using the logistic regression in analysis of results from statistical observations (in Ukrainian). Statistics of Ukraine: scientific bulletin of the National Academy of of Statistics, Accounting and Auditing, 3(94), 4–11.
  14. Kononova K. Yu. (2020). Machine learning: methods and models: a textbook (in Ukrainian). Kharkiv:
  15. V. N. Karazin Kharkiv National University, 2020.
  16. Krysik A. (2021). Amazon’s Product Recommendation System In 2021: How Does The Algorithm Of The eCommerce Giant Work?
  17. Chong D. (2020). Deep Dive into Netflix’s Recommender System. dive-into-netflixs-recommender-system-341806ae3b48.
  18. online store (2023). Marketplace. Work with buyers (in Ukrainian). ua/p221-customer-care.html.
  19. online store (2023). Rules for publishing user comments and reviews on the website (in Ukrainian).
  20. Kulakowski K. (2020). Understanding the Analytic Hierarchy Process. New York: Chapman and Hall.
  21. The Creately Visual Platform. (2022). Use Case Diagram Relationships Explained with Examples
  22. Ruder S. (2017). An overview of gradient descent optimization algorithms. arXiv:1609.04747v2 [cs.LG], 1-14.
  23. Hu J., Chen X., Zheng L., Zhang L., Li H. (2021). The Barzilai – Borwein Method for distributed optimization over unbalanced directed networks. Engineering Applications of Artificial Intelligence. Vol. 99. Elsevier.