Work

Application of Clustering Methods in Recommender Systems for User Behavior Analysis

Year: 2025

Type: conference-paper

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.

Source: Environment Technology Resources Proceedings of the International Scientific and Practical Conference

DOI: 10.17770/etr2025vol2.8611

Authors: Iryna Kyrychenko, Yehor Nesterenko, Anastasiya Chupryna, Loreta Savulioniene, Paulius Sakalys

Institutions: Kharkiv National University of Radio Electronics, Vilnius University of Applied Sciences

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4411132352

FWCI:

Cites: 5

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Related to: 10