Clustering Methods for Adaptive e-Commerce User Interfaces

Typical online shops have one interface provided to all users, regardless of their use of the shop. Meanwhile, user behavior varies and therefore different interfaces could be provided to different user groups. Various methods can be used to cluster users, including those using artificial intelligence (AI) methods. AI-based personalization allows e-commerce businesses to provide tailored recommendations to each individual customer based on preferences, purchase history, and behavior on the website. This article presents a study of the impact of an AI-based clustering method on the effectiveness of a dedicated user interface implemented and delivered to the customers of an e-shop. The first study included five methods, and two of them - agglomerative clustering and K-means clustering - were selected for detailed analysis. For both of these methods, an in-depth research was carried out and the impact of the clustering method on the quality of user clusters, as measured by the effectiveness of the dedicated interface in relation to the effectiveness of the default interface, was verified.