Recommender Systems in E-commerce: State-of-the-art Methods for Improving Personalized Recommendations in Online Shopping Platforms

Abstract

Recommender systems have become a pivotal component in the success of online shopping platforms, providing personalized suggestions that enhance the user experience and drive sales. This review article explores the state-of-the-art methods for improving personalized recommendations in e-commerce, focusing on algorithms and models that leverage user behavior, product characteristics, and contextual information. We discuss collaborative filtering, content-based filtering, and hybrid approaches, highlighting recent advances in machine learning frameworks such as deep learning, reinforcement learning, and graph-based methods. Furthermore, we address challenges related to scalability, diversity, and privacy, and examine innovative solutions proposed in recent literature. This comprehensive overview aims to provide insights into the current methodologies and potential future directions in the development of effective recommender systems for e-commerce