Machine Learning for Recommender Systems: A Review of Recent Developments and Applications

Abstract

Recommender systems are ubiquitous in today's world, enabling personalized and targeted recommendations across a wide range of applications such as e-commerce, social networks, and online content platforms. Machine learning (ML) techniques have played a critical role in the development of recommender systems, enabling the generation of accurate and effective recommendations by leveraging user data and behavior. In this paper, we provide a comprehensive review of recent developments and applications of ML for recommender systems. We first introduce the basic concepts of recommender systems and then survey the state-of-the-art ML techniques used in recommender systems, including collaborative filtering, content-based filtering, and hybrid approaches. We discuss recent advancements in ML for recommender systems, such as deep learning, matrix factorization, and graph-based approaches. We also provide an overview of the evaluation metrics used to assess the performance of recommender systems and highlight some open research challenges in this field. Finally, we present some promising applications of ML for recommender systems, including personalized health recommendations and intelligent transportation systems.