Multi-criteria selection of data clustering methods for e-commerce personalization

Abstract

E-commerce platforms increasingly rely on personalization to improve the user experience and drive sales, requiring efficient data clustering methods to segment users based on their behavior and preferences. However, due to the many data clustering techniques available, the key decision problem is to choose the optimal grouping method. Decision making based on single decision factors, although widely used, can lead to wrong decisions, so it is worth considering multi-criteria analysis tailored to the specifics of e-commerce customer clustering. Through extensive experiments on real e-commerce datasets, the study demonstrates the strengths and limitations of selected data clustering techniques (including the Approximated Gaussian Mixture Model, which was found to be superior to the classical Gaussian Mixture Model), considering different decision criteria related to various aspects of quality. The results provide valuable insights for e-commerce practitioners seeking to optimize their personalization strategies and ultimately suggest that a novel adaptation of the PROMETHEE II method can provide a robust framework for making informed decisions about selection of data clustering algorithms.