Analysis of clustering algorithms for product recommendations

Abstract

Relevance. In today's world, where a wide range of goods and services are available, the task of providing personalized recommendations for selecting the right one is becoming an increasingly important in many areas, including e-commerce and online platforms. Expert recommendation systems powered by search and clustering algorithms have the potential to significantly improve the user experience by offering relevant and personalized product suggestions. One of the key advantages of using clustering algorithms for recommender systems is the ability to predict the similarity of objects based on their compliance with a certain characteristic, which makes it possible to implement an effective search for products by characteristics. As a result, it allows dividing an user base into separate subgroups that can represent different market segments, preference groups, and the target audience of certain products. Identification of problems and shortcomings of such systems helps to improve algorithms, which leads to more accurate forecasts and increased sales.

Objective. The purpose of this article is to analyze the effectiveness of using cluster analysis methods in the tasks of generating recommendations.

Research methods. Comparative analysis, experiment.

Results. The effectiveness of clustering algorithms of different types (k-means++, Mean Shift and HDBSCAN) for providing product recommendations based on the assessment of the percentage of compliance with the user's request, the use of RAM, and the query execution time has been analyzed. The k-means++ algorithm showed the best performance among the tested algorithms.

Conclusions. Our analysis confirms the effectiveness of using cluster analysis methods in recommender systems. Identification of problems and shortcomings of such systems allows improving algorithms, which leads to more accurate forecasts and increased sales of companies.