mengfeizhang820/Paperlist-for-Recommender-Systems
Recommender Systems Paperlist that I am interested in
This is a curated collection of research papers and associated code for building and understanding recommender systems. It organizes academic work by various approaches, such as deep learning, collaborative filtering, and session-based methods, providing direct links to PDFs and code implementations. It's a valuable resource for researchers, data scientists, or engineers who are building or evaluating recommendation engines for products, content, or services.
449 stars. No commits in the last 6 months.
Use this if you are a researcher or practitioner looking for cutting-edge algorithms and academic insights to develop or improve recommendation features in your products.
Not ideal if you need a plug-and-play solution or a high-level guide to implementing recommender systems without diving into academic papers and complex model architectures.
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Jul 29, 2021
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