Artificial intelligence, Recommendation system, Collaborative filtration, Content filtration, Hybrid algorithm, literary service, web application, switching, feature enhancement


Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requests
in certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are one
of the types of artificial intelligence technologies for predicting parameters and capabilities.
Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by the
service itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system for
searching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis of
their assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractive
to the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personal
recommendations to customers according to their preferences.
The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages.
Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in one
system and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of the
algorithms separately.
A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system of
recommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literature
service was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system was
tested successfully and showed high efficiency.

Author Biographies

Mariia Kozulia, National Technical University "Kharkiv Polytechnic Institute"

candidate of engineering science, National Technical University "Kharkiv Polytechnic Institute",associated professor of the Department of Software Engineering and Management Information Technologies, Kharkiv, Ukraine

Vladyslava Sushko, National Technical University "Kharkiv Polytechnic Institute"

National Technical University "Kharkiv Polytechnic Institute", student, master; Kharkiv, Ukraine


Isinkaye F. O., Folajimi Y. O., Ojokoh B. A. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal. 2015, vol. 16, issue 3, pp. 261–273.

Venkatesan M., Thangadurai K. History and overview of the recommender systems. Collaborative Filtering Using Data Mining and Analysis. 2016, pp. 74–99.

Shardanand U., Maes P. Social Information Filtering: Algorithms for Automating «Word of Mouth». Proceedings of the SIGCHI conference on Human factors in computing systems. [Proceedings of the ACM CHI 95 Human Factors in Computing Systems Conference] New York, NY, USA.1995, pp. 210–217.

Hill W., Stead L., Rosenstein M., Furnas G. Recommending and Evaluating Choices in a Virtual Community of use. Proceedings of the SIGCHI conference on Human factors in computing systems. [Proceedings of the ACM CHI 95 Human Factors in Computing Systems Conference] New York, NY, USA. 1995, pp. 194–201.

Resnick P., Iakovou N., Sushak M., Bergstrom P., Riedl J. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the 1994 ACM conference on Computer supported cooperative work, [CSCW '94 Computer Science]. 1994, pp. 175–186.

Celma O. Music Recommendation and Discovery. Springer [Springer. Computer science], Berlin, Heidelberg, 2010, pp. 43–85.

Adomavicius G., Tuzhilin A. 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 [IEEE Computer Society]. 2005, Vol. 17, issue 6, pp. 734–749.

Burke, R., Felfernig, A., & Göker, M. H. Recommender Systems: An Overview. AI Magazine. 2011, no. 32(3), pp. 13–18.

How Reddit ranking algorithms work, URL: (accessed 27.03.2021).

Shahbazi, Z. Byun, Y.C. Product Recommendation Based on Content-based Filtering Using XGBoost Classifier. International Journal of Advanced Science and Technology. 29(04), pp. 6979– 6988. URL: view/28099 (accessed 27.03.2021).

K. Madadipouya, S. Chelliah. A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations. BRAIN [Broad Research in Artificial Intelligence and Neuroscience], 2017, vol. 8, issue 2, pp. 109–124.

Phys J. Summary of recommendation system development, IOP Conf. Series: Journal of Physics: Conf. Series 1187, pp. 1–5.

Foreign online store of literature and training videos, URL: (accessed 25.03.2021).

Free online cinema, (accessed 25.03.2021).

Ricci F., Rokach L., Shapira B., Kantor P.B. Recommender Systems Handbook. Springer US, 2011. 842 P.

Aggarwal C. C. Data mining. Springer International Publishing, 2015. 734 p.

Portugal I., Alencar P., Cowan D. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications. 2018, vol. 97, pp. 205–227.

Li X., Wang Z., Wang L., Hu R., Zhu Q. A multidimensional context-aware recommendation approach based on improved random forest algorithm. IEEE Access. 2018, vol. 6, pp. 45071– 45085.

Wang X., Wen J., Luo F., Zhou W., Ren H. Personalized recommendation system based on support vector machine and particle swarm optimization. International Conference on Knowledge Science [International Conference on Knowledge Science, Engineering and Management]. 2015, pp. 489–495.

Marović M., Mihoković M., Mikša M., Pribil S., Tus A. Automatic movie ratings prediction using machine learning. 34th International Convention on Information and Communication Technology, Electronics and Microelectronics. 2011, pp. 1640–1645.

Çano E., Morisio M. Hybrid recommender systems: a systematic literature review. Intelligent Data Analysis. 2017, vol. 21, no. 6, pp. 1487–1524.

Fayyaz Z., Ebrahimian M., Nawara D. Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Multidisciplinary Digital Publishing Institute. 2020, vol.10, issue 21, p. 7748.

Beladev M., Rokach L., Shapira, B. Recommender systems for product bundling. Knowl. Based Syst. 2015, vol. 111, pp. 193–206.

Naveen G., Naidu A., Thirumala Dr. B. Comparative Study on Artificial Intelligence and Expert Systems. International Research Journal of Engineering and Technologyю 2019, vol. 6, issue 2, pp. 1980–1986.

Vilnius K. Intelligent Decision Support Systems. Biometric and Intelligent Decision Making Supportю 2015, pp 31–85.

Peter B. Keenan, Piotr J. Spatial Decision Support Systems: Three decades on. Decision Support Systems. 2019, vol. 116, pp. 64–76.

Waila, P., Singh, V., Singh, M. A Scientometric Analysis of Research in Recommender Systems. J Scientometric Res. 2016, vol. 5, issue 1, pp. 71– 84.

Zhang Q., Jie Lu. Artificial intelligence in recommender systems. Complex & Intelligent Systems. 2020, vol. 7, pp. 439–457.

Luy Z., Dou Z., Lianz J. Content-Based Collaborative Filtering for News Topic Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 2015, no. 29(1), pp. 217–223.

Li X., Xing J., Wang H., Zheng L., Jia S., Wang Q. A Hybrid Recommendation Method Based on Feature for Offline Book Personalization. Journal of Computers. 2019, vol. 30, no. 5, pp. 1–17.

Lytvynenko V., Lurie I., Krejci J. Two Step Density-Based ObjectInductive Clustering Algorithm. Workshop Proceedings of the 8th International Conference on “Mathematics. Information Technologies. Education”. 2019, pp. 117–135.

Smith J., Weeks D., Freeman J., Jacob M., Magerko B., Towards a Hybrid Recommendation System for a Sound Library. IUI Workshops’19 [Joint Proceedings of the ACM IUI 2019 Workshops]. 2019, vol. 2327, 6 p.

Chyrun L., Burov Y., Rusyn B. Web Resource Changes Monitoring System Development. Workshop Proceedings of the 8th International Conference on “Mathematics. Information Technologies. Education”. 2019, pp. 255–273.




How to Cite

Kozulia, M., & Sushko, V. (2021). DETERMINE RECOMMENDATION SYSTEMS TO SEARCH FOR BOOKS BY PREFERENCES OF WEB USERS. Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, (2 (6), 73–80.