A Comprehensive Survey on the Evolution and Deep Learning Techniques in Recommender Systems
S.C. Smitha, K S Harishkumar · 2025
This survey paper provides a comprehensive review of the evolution, methodologies, and current state-of-the-art in recommender systems (RS), which are pivotal in managing the surge of data across various digital platforms, particularly in e-commerce.It traces the development of RS from early collaborative and content-based filtering techniques to sophisticated machine learning models that leverage deep learning, hybrid methods, and complex algorithms to enhance prediction accuracy and user interaction.Despite significant strides in the field, recommender systems continue to grapple with challenges including the cold-start problem, data sparsity, synonymy, and scalability, as well as ensuring user privacy in an increasingly security-conscious digital environment.The paper evaluates different RS architectures and their effectiveness across diverse sectors, highlighting how advancements like knowledge-based systems, attention mechanisms, and temporal models address specific shortcomings.We critically analyse existing gaps in technology and methodology, proposing future directions for research that include enhancing personalization features, improving adaptive learning capabilities, and integrating ethical AI practices.