fuxuemingzhu/Summary-of-Recommender-System-Papers
阅读过的推荐系统论文的归类总结，持续更新中…
This collection of categorized papers summarizes and integrates current research in recommender systems, making it easier to grasp key advancements. It takes a wide range of academic papers on recommendation algorithms as input and organizes them by common themes like collaborative filtering, matrix factorization, and deep learning approaches. Anyone building, researching, or needing to understand recommendation engines for products, content, or services would find this useful.
382 stars. No commits in the last 6 months.
Use this if you need to quickly understand the landscape of recommender system research, identify core algorithms, or explore different approaches for building recommendation engines.
Not ideal if you are looking for ready-to-use code implementations or a step-by-step guide to building a specific recommender system.
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Mar 09, 2019
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