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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2025-04-12,本仓库收集汇总了推荐系统领域相关论文共**900**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2025-04-14,本仓库收集汇总了推荐系统领域相关论文共**901**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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- [Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation](Industry/Regression/Conditional%20Quantile%20Estimation%20for%20Uncertain%20Watch%20Time%20in%20Short-Video%20Recommendation.pdf)
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- [CREAD - A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems](Industry/Regression/CREAD%20-%20A%20Classification-Restoration%20Framework%20with%20Error%20Adaptive%20Discretization%20for%20Watch%20Time%20Prediction%20in%20Video%20Recommender%20Systems.pdf)
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- [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation](Industry/Regression/Deconfounding%20Duration%20Bias%20in%20Watch-time%20Prediction%20for%20Video%20Recommendation.pdf)
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- [Explicit Uncertainty Modeling for Video Watch Time Prediction](Industry/Regression/Explicit%20Uncertainty%20Modeling%20for%20Video%20Watch%20Time%20Prediction.pdf)
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- [Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](Industry/Regression/Tree%20based%20Progressive%20Regression%20Model%20for%20Watch-Time%20Prediction%20in%20Short-video%20Recommendation.pdf)
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#### AutomaticPlaylistContinuation
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- [A hybrid two-stage recommender system for automatic playlist continuation](Industry/AutomaticPlaylistContinuation/A%20hybrid%20two-stage%20recommender%20system%20for%20automatic%20playlist%20continuation.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 2025-04-12, **900** papers related to recommendation system have been collected and summarized in this repo,
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1. Up to 2025-04-14, **901** 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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- [Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation](Industry/Regression/Conditional%20Quantile%20Estimation%20for%20Uncertain%20Watch%20Time%20in%20Short-Video%20Recommendation.pdf)
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- [CREAD - A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems](Industry/Regression/CREAD%20-%20A%20Classification-Restoration%20Framework%20with%20Error%20Adaptive%20Discretization%20for%20Watch%20Time%20Prediction%20in%20Video%20Recommender%20Systems.pdf)
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- [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation](Industry/Regression/Deconfounding%20Duration%20Bias%20in%20Watch-time%20Prediction%20for%20Video%20Recommendation.pdf)
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- [Explicit Uncertainty Modeling for Video Watch Time Prediction](Industry/Regression/Explicit%20Uncertainty%20Modeling%20for%20Video%20Watch%20Time%20Prediction.pdf)
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- [Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](Industry/Regression/Tree%20based%20Progressive%20Regression%20Model%20for%20Watch-Time%20Prediction%20in%20Short-video%20Recommendation.pdf)
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#### AutomaticPlaylistContinuation
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- [A hybrid two-stage recommender system for automatic playlist continuation](Industry/AutomaticPlaylistContinuation/A%20hybrid%20two-stage%20recommender%20system%20for%20automatic%20playlist%20continuation.pdf)

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