CLUSTERING METHODS FOR USER SEGMENTATION IN RECOMMENDER SYSTEMS

With the development of information technologies and the constant growth of digital content, the issue of providing highquality personalized recommendations is becoming an important aspect of modern information systems. The development and implementation of recommender systems that can effectively adapt suggestions to individual user preferences is a key area of research in data mining. Recently, more and more attention has been paid to algorithms that provide more accurate data segmentation to improve the quality of recommendations. In particular, clustering methods allow grouping users based on similar behavioral characteristics, which positively affects the accuracy of the recommendations provided and contributes to their relevance. In the course of this study, modern approaches to the use of clustering methods in the context of creating recommender systems are considered. Considerable attention is paid to the principles of adapting recommendations based on the analysis of user behavior and preferences, as well as the possibilities of improving the accuracy of forecasts through the use of data grouping. Segmentation approaches that allow creating consistent and accurate forecasts for different categories of users, taking into account their unique features and preferences, are considered. The prospects of using different data processing strategies and techniques to improve the quality of recommendations are analyzed. The results of the study emphasize the importance of choosing an appropriate data analysis method to ensure the relevance of the content received by users. It has been determined that effective clustering can significantly improve not only the accuracy of recommendations but also the overall user experience, as it helps to identify hidden patterns in behavior and preferences, which in turn contributes to a deeper understanding of user needs and increases the likelihood of their satisfaction with the service. Thus, this study has significant potential for the development and improvement of recommender systems in various fields.