Abstract: The spread of the Web and the digitalization of human society has led to the emergence of e-commerce sites. The remarkable increase in the amount of data produced by digital and automated devices forces the use of intelligent algorithms capable of processing the collected data in order to extract information. In particular, machine learning algorithms give the possibility to implement automatic models to process data and provide personalized suggestions. The advanced recommender systems are based on these models that make companies, which use the e-commerce channel, able to provide the users with suggestions on products they may be interested in. This paper proposes a model of hybrid recommender system based on the use of clustering algorithms and XGBoost, respectively, to perform a preliminary segmentation of item-customer data and predict user preference. The implemented model is discussed and preliminarily validated through a test performed using the data of a statistical sample made up of regular users of an e-commerce site.
Keywords: Recommender System (RS), Artificial Intelligence (AI), Machine Learning, XGBoost, K-Means.
