APPLICATION OF CLUSTERING METHODS IN RECOMMENDER SYSTEMS FOR USER BEHAVIOR ANALYSIS

Authors

  • Iryna Kyrychenko Department of Software Engineering, Kharkiv National University of Radio Electronics (UA)
  • Yehor Nesterenko Department of Software Engineering, Kharkiv National University of Radio Electronics (UA)
  • Anastasiya Chupryna Department of Software Engineering, Kharkiv National University of Radio Electronics (UA)
  • Loreta Savulioniene Faculty of Electronics and Informatics, Vilniaus Kolegija (LT)
  • Paulius Sakalys Faculty of Electronics and Informatics, Vilniaus Kolegija (LT)

DOI:

https://doi.org/10.17770/etr2025vol2.8611

Keywords:

collaborative filtering, k-means clustering, recommender systems, user segmentation

Abstract

Recommender systems are crucial in personalizing digital experiences by predicting user preferences. This paper examines the application of the k-means clustering algorithm in recommender systems to segment users based on behavioural patterns, enhancing recommendation accuracy and efficiency. The study also compares k-means with other clustering techniques, analyzing their advantages and limitations in handling sparsity and the cold start problem. The results highlight the effectiveness of k-means for improving user segmentation and optimizing recommendation strategies.

References

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Published

08.06.2025

Issue

Vol. 2 (2025): Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference. Volume 2

Section

Information Technologies

License

Copyright (c) 2025 Iryna Kyrychenko, Yehor Nesterenko, Anastasiya Chupryna, Loreta Savulioniene, Paulius Sakalys

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
I. Kyrychenko, Y. Nesterenko, A. Chupryna, L. Savulioniene, and P. Sakalys, “APPLICATION OF CLUSTERING METHODS IN RECOMMENDER SYSTEMS FOR USER BEHAVIOR ANALYSIS”, ETR, vol. 2, pp. 177–180, Jun. 2025, doi: 10.17770/etr2025vol2.8611.