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README.md

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# 推荐系统相关论文汇总
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([English Version is Here](/README_EN.md))
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## 介绍
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1. 截至2024-10-28,本仓库收集汇总了推荐系统领域相关论文共**849**篇,涉及:**召回****粗排****精排****重排****多任务****多场景****多模态****冷启动****校准**
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1. 截至2024-11-01,本仓库收集汇总了推荐系统领域相关论文共**850**篇,涉及:**召回****粗排****精排****重排****多任务****多场景****多模态****冷启动****校准**
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**纠偏****多样性****公平性****反馈延迟****蒸馏****对比学习****因果推断****Look-Alike****Learning-to-Rank****强化学习**等领域,本仓库会跟踪业界进展,持续更新。
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2. 因文件名特殊字符的限制,故论文title中所有的`:`都改为了`-`,检索时请注意。
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3. 文件名前缀中带有`[]`的,表明本人已经通读过,第一个`[]`中为论文年份,第二个`[]`中为发表机构或公司(可选),第三个`[]`中为论文提出的model或method的简称(可选)。
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- [Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems](Re-Rank/Coverage%2C%20Redundancy%20and%20Size-Awareness%20in%20Genre%20Diversity%20for%20Recommender%20Systems.pdf)
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- [Discrete Conditional Diffusion for Reranking in Recommendation](Re-Rank/Discrete%20Conditional%20Diffusion%20for%20Reranking%20in%20Recommendation.pdf)
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- [DEAR - Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](Re-Rank/DEAR%20-%20Deep%20Reinforcement%20Learning%20for%20Online%20Advertising%20Impression%20in%20Recommender%20Systems.pdf)
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- [Do Not Wait - Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](Re-Rank/Do%20Not%20Wait%20-%20Learning%20Re-Ranking%20Model%20Without%20User%20Feedback%20At%20Serving%20Time%20in%20E-Commerce.pdf)
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- [GenDeR - A Generic Diversified Ranking Algorithm](Re-Rank/GenDeR%20-%20A%20Generic%20Diversified%20Ranking%20Algorithm.pdf)
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- [GRN - Generative Rerank Network for Context-wise Recommendation](Re-Rank/GRN%20-%20Generative%20Rerank%20Network%20for%20Context-wise%20Recommendation.pdf)
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- [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](Re-Rank/Globally%20Optimized%20Mutual%20Influence%20Aware%20Ranking%20in%20E-Commerce%20Search.pdf)

README_EN.md

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# Summary of Papers Related to Recommendation System
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## Introduce
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1. Up to 2024-10-28, **849** papers related to recommendation system have been collected and summarized in this repo,
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1. Up to 2024-11-01, **850** papers related to recommendation system have been collected and summarized in this repo,
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including: **Match**, **Pre-Rank**, **Rank**, **Re-Rank**, **Multi-Task**, **Multi-Scenario**, **Multi-Modal**, **Cold-Start**, **Calibration**,
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**Debias**, **Diversity**, **Fairness**, **Feedback-Delay**, **Distillation**, **Contrastive Learning**, **Casual Inference**,
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**Look-Alike**, **Learning-to-Rank**, **Reinforcement Learning** and other fields, the repo will track the industry progress and update continuely.
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- [Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems](Re-Rank/Coverage%2C%20Redundancy%20and%20Size-Awareness%20in%20Genre%20Diversity%20for%20Recommender%20Systems.pdf)
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- [Discrete Conditional Diffusion for Reranking in Recommendation](Re-Rank/Discrete%20Conditional%20Diffusion%20for%20Reranking%20in%20Recommendation.pdf)
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- [DEAR - Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](Re-Rank/DEAR%20-%20Deep%20Reinforcement%20Learning%20for%20Online%20Advertising%20Impression%20in%20Recommender%20Systems.pdf)
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- [Do Not Wait - Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](Re-Rank/Do%20Not%20Wait%20-%20Learning%20Re-Ranking%20Model%20Without%20User%20Feedback%20At%20Serving%20Time%20in%20E-Commerce.pdf)
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- [GenDeR - A Generic Diversified Ranking Algorithm](Re-Rank/GenDeR%20-%20A%20Generic%20Diversified%20Ranking%20Algorithm.pdf)
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- [GRN - Generative Rerank Network for Context-wise Recommendation](Re-Rank/GRN%20-%20Generative%20Rerank%20Network%20for%20Context-wise%20Recommendation.pdf)
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- [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](Re-Rank/Globally%20Optimized%20Mutual%20Influence%20Aware%20Ranking%20in%20E-Commerce%20Search.pdf)

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