Deep-Learning-for-Recommendation-Systems

Why we included this project

If you're building or studying neural approaches to recommendations, this index saves you weeks of scattered searching. It gathers the foundational papers that shaped deep recommender systems, from collaborative-filtering autoencoders and Neural Collaborative Filtering to YouTube's deep ranking and Wide & Deep for click-through prediction, and links each one to its original source and often to a working code repository. The list also folds in related blog posts plus a section of open-source libraries and datasets, so it works as a quick launchpad when you need a reference implementation or a baseline to compare against. The repo itself isn't software you deploy, but it's a genuinely useful map for engineers, researchers, and students deciding which deep learning architecture to try next in their own recommendation pipeline.