Artificial intelligence as a modern tool for enhancing recommender systems

Abstract

This article explores modern recommender systems built upon Artificial Intelligence, Machine Learning, and Deep Learning technologies. The author discusses how these methods are employed by digital platforms to enhance user experiences and improve business outcomes. The pivotal role of personalisation, driven by extensive user behaviour analysis, is underscored, and techniques to process large, diverse datasets are examined. Special attention is given to how Deep Learning approaches and Reinforcement Learning enable the dynamic adaptation of recommendations. Additionally, the article addresses ethical considerations such as the formation of «filter bubbles» and the need for algorithmic transparency. Conclusively, it highlights that AI-powered solutions have become integral to recommender systems, offering competitive advantages and meeting user needs in an era of information overload.