On the role of user segmentation in recommender systems’ performances

Recommender systems are designed to suggest items for purchase or examination based on user preferences. In a variety of industries, such as e-commerce, tourism, social media, etc., these systems are frequently employed. However, they do have certain limits because of the vast amount of data they use. To enhance suggestion quality, artificial learning models have become prevalent in this field, along with data filtering techniques. In this study, as a data filtering technique, a user segmentation similar to well-known Recency-Frequency-Monetary is presented with the goal of examining how the use of user-segmented data affects the precision and caliber of recommendations produced by various algorithms. From the most fundamental popularity-based techniques to the most recent and sophisticated graph neural network approaches, the experiments are performed with eight different approaches. The results of the comparative analysis demonstrate that recommendation performance depends not only on user segmentation but also on other factors of the chosen method or data set. Nevertheless, user segmentation can improve the performance of some algorithms.