Reco-papers is a curated collection of academic papers and industry resources on recommendation systems.
The collection addresses the need for practitioners and researchers to access foundational and contemporary work in recommendation systems in one organized location. It gathers papers spanning retrieval and ranking approaches, deep learning methods, exploration-exploitation strategies, and reinforcement learning applications in recommendations. The repository organizes materials by topic area, making it easier to navigate research across different recommendation system components and techniques.
This resource suits researchers, machine learning engineers, and practitioners building or studying recommendation systems who want a structured entry point into the academic literature. It works well for those seeking to understand both classical approaches and modern deep learning techniques in the field.
The project maintains an active curation model, with materials drawn from internet sources and organized by topic. The maintainer actively welcomes discussion and contributions from others interested in recommendation systems, indicating ongoing engagement with the community around this resource.
