Recommender Systems Evolution: Challenges and Future Directions

Recommender systems have become a foundational component in modern digital platforms, shaping how users discover information, products, and services. Their ability to analyze user behavior, model preferences, and anticipate future needs has made them indispensable across a wide range of domains, including e-commerce, entertainment, education, and healthcare. As these systems have continued to evolve, the growing complexity of user data, content types, and application requirements has driven the development of increasingly advanced recommendation techniques. This survey offers a comprehensive examination of that evolution, tracing the field’s progression from early collaborative and content-based methods to more sophisticated, explainable, and multimodal knowledge graph–based models. It reviews major methodologies, architectural trends, and technical innovations, while also highlighting persistent challenges and emerging research directions. Through this work, we aim to provide researchers and practitioners with a clear, structured, forward-looking reference for designing next-generation recommender systems that are accurate, robust, knowledge-aware, multimodal, and fully interpretable.