Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems

Abstract

Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E‐commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing the user's engagement on these applications. This study offers a comprehensive review of recommendation systems, covering their types, fundamental techniques, and emerging trends, with a focus on the predictive models and algorithms that power personalization. This study shows how in comparison to traditional collaborative and content‐based recommendation systems‐building techniques, the novel approaches of deep learning, graph‐based techniques, meta‐learning, few‐shot learning, exploration, and federated learning offer promising prospects to improve recommendation systems' scalability, privacy‐preserving abilities, and accuracy. These advanced methods deliver more diverse, context‐aware, and personalized recommendations by leveraging large‐scale data and complex predictive algorithms. Furthermore, this paper depicts forthcoming trajectories in the field of recommendation systems, including the adoption of graph‐based approaches, federated learning, and the exploration of ethical considerations. By mapping the current landscape of prediction‐driven personalization and identifying emerging trends, this review serves as a valuable resource for scholars and practitioners seeking to deepen their understanding of the field and drive innovation in recommendation systems. Readers can expect to gain insights into both foundational and cutting‐edge techniques and how these can shape the future of personalized recommendations.