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Mr-Nineteen · Nov 20, 2023 · b55264c · b55264c
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diff --git a/Industry/Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation.pdf b/Industry/Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation.pdf
diff --git a/Industry/Inverse Learning with Extremely Sparse Feedback for Recommendation.pdf b/Industry/Inverse Learning with Extremely Sparse Feedback for Recommendation.pdf
diff --git a/Multi-Scenario/Mixed Attention Network for Cross-domain Sequential Recommendation.pdf b/Multi-Scenario/Mixed Attention Network for Cross-domain Sequential Recommendation.pdf
diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 # 推荐系统相关论文汇总
 ([English Version is Here](/README_EN.md))
 ## 介绍
-1. 截至2023-11-16，本仓库收集汇总了推荐系统领域相关论文共**802**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**，
+1. 截至2023-11-20，本仓库收集汇总了推荐系统领域相关论文共**805**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**，
 **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。
 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。
 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)。
@@ -124,11 +124,13 @@
 - [FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction](Industry/FinalMLP%20-%20An%20Enhanced%20Two-Stream%20MLP%20Model%20for%20CTR%20Prediction.pdf)
 - [GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](Industry/GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
 - [Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](Industry/Graph-Based%20Model-Agnostic%20Data%20Subsampling%20for%20Recommendation%20Systems.pdf)
+- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](Industry/Group-Aware%20Interest%20Disentangled%20Dual-Training%20for%20Personalized%20Recommendation.pdf)
 - [Generative Flow Network for Listwise Recommendation](Industry/Generative%20Flow%20Network%20for%20Listwise%20Recommendation.pdf)
 - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](Industry/Hierarchically%20Fusing%20Long%20and%20Short-Term%20User%20Interests%20for%20Click-Through%20Rate%20Prediction%20in%20Product%20Search.pdf)
 - [Hybrid Interest Modeling for Long-tailed Users](Industry/Hybrid%20Interest%20Modeling%20for%20Long-tailed%20Users.pdf)
 - [Hierarchical Gating Networks for Sequential Recommendation](Industry/Hierarchical%20Gating%20Networks%20for%20Sequential%20Recommendation.pdf)
 - [HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction](Industry/HIEN%20-%20Hierarchical%20Intention%20Embedding%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
+- [Inverse Learning with Extremely Sparse Feedback for Recommendation](Industry/Inverse%20Learning%20with%20Extremely%20Sparse%20Feedback%20for%20Recommendation.pdf)
 - [Improving Pairwise Learning for Item Recommendation from Implicit Feedback](Industry/Improving%20Pairwise%20Learning%20for%20Item%20Recommendation%20from%20Implicit%20Feedback.pdf)
 - [Improving Recommendation Quality in Google Drive](Industry/Improving%20Recommendation%20Quality%20in%20Google%20Drive.pdf)
 - [Incorporating Social-aware User Preference for Video Recommendation](Industry/Incorporating%20Social-aware%20User%20Preference%20for%20Video%20Recommendation.pdf)
@@ -630,6 +632,7 @@
 - [Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](Multi-Scenario/Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf)
 - [KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](Multi-Scenario/KEEP%20-%20An%20Industrial%20Pre-Training%20Framework%20for%20Online%20Recommendation%20via%20Knowledge%20Extraction%20and%20Plugging.pdf)
 - [Leaving No One Behind - A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](Multi-Scenario/Leaving%20No%20One%20Behind%20-%20A%20Multi-Scenario%20Multi-Task%20Meta%20Learning%20Approach%20for%20Advertiser%20Modeling.pdf)
+- [Mixed Attention Network for Cross-domain Sequential Recommendation](Multi-Scenario/Mixed%20Attention%20Network%20for%20Cross-domain%20Sequential%20Recommendation.pdf)
 - [Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services](Multi-Scenario/Multi-Graph%20based%20Multi-Scenario%20Recommendation%20in%20Large-scale%20Online%20Video%20Services.pdf)
 - [Multi-Scenario Ranking with Adaptive Feature Learning](Multi-Scenario/Multi-Scenario%20Ranking%20with%20Adaptive%20Feature%20Learning.pdf)
 - [Personalized Transfer of User Preferences for Cross-domain Recommendation](Multi-Scenario/Personalized%20Transfer%20of%20User%20Preferences%20for%20Cross-domain%20Recommendation.pdf)

diff --git a/README_EN.md b/README_EN.md
@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2023-11-16, **802** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2023-11-20, **805** papers related to recommendation system have been collected and summarized in this repo, 
 including: **Match**, **Pre-Rank**, **Rank**, **Re-Rank**, **Multi-Task**, **Multi-Scenario**, **Multi-Modal**, **Cold-Start**, **Calibration**, 
 **Debias**, **Diversity**, **Fairness**, **Feedback-Delay**, **Distillation**, **Contrastive Learning**, **Casual Inference**, 
 **Look-Alike**, **Learning-to-Rank**, **Reinforcement Learning** and other fields, the repo will track the industry progress and update continuely.
@@ -131,11 +131,13 @@ I will remove it immediately after verification.
 - [FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction](Industry/FinalMLP%20-%20An%20Enhanced%20Two-Stream%20MLP%20Model%20for%20CTR%20Prediction.pdf)
 - [GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction](Industry/GateNet%20-%20Gating-Enhanced%20Deep%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
 - [Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](Industry/Graph-Based%20Model-Agnostic%20Data%20Subsampling%20for%20Recommendation%20Systems.pdf)
+- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](Industry/Group-Aware%20Interest%20Disentangled%20Dual-Training%20for%20Personalized%20Recommendation.pdf)
 - [Generative Flow Network for Listwise Recommendation](Industry/Generative%20Flow%20Network%20for%20Listwise%20Recommendation.pdf)
 - [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](Industry/Hierarchically%20Fusing%20Long%20and%20Short-Term%20User%20Interests%20for%20Click-Through%20Rate%20Prediction%20in%20Product%20Search.pdf)
 - [Hybrid Interest Modeling for Long-tailed Users](Industry/Hybrid%20Interest%20Modeling%20for%20Long-tailed%20Users.pdf)
 - [Hierarchical Gating Networks for Sequential Recommendation](Industry/Hierarchical%20Gating%20Networks%20for%20Sequential%20Recommendation.pdf)
 - [HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction](Industry/HIEN%20-%20Hierarchical%20Intention%20Embedding%20Network%20for%20Click-Through%20Rate%20Prediction.pdf)
+- [Inverse Learning with Extremely Sparse Feedback for Recommendation](Industry/Inverse%20Learning%20with%20Extremely%20Sparse%20Feedback%20for%20Recommendation.pdf)
 - [Improving Pairwise Learning for Item Recommendation from Implicit Feedback](Industry/Improving%20Pairwise%20Learning%20for%20Item%20Recommendation%20from%20Implicit%20Feedback.pdf)
 - [Improving Recommendation Quality in Google Drive](Industry/Improving%20Recommendation%20Quality%20in%20Google%20Drive.pdf)
 - [Incorporating Social-aware User Preference for Video Recommendation](Industry/Incorporating%20Social-aware%20User%20Preference%20for%20Video%20Recommendation.pdf)
@@ -637,6 +639,7 @@ I will remove it immediately after verification.
 - [Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space](Multi-Scenario/Improving%20Multi-Scenario%20Learning%20to%20Rank%20in%20E-commerce%20by%20Exploiting%20Task%20Relationships%20in%20the%20Label%20Space.pdf)
 - [KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](Multi-Scenario/KEEP%20-%20An%20Industrial%20Pre-Training%20Framework%20for%20Online%20Recommendation%20via%20Knowledge%20Extraction%20and%20Plugging.pdf)
 - [Leaving No One Behind - A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](Multi-Scenario/Leaving%20No%20One%20Behind%20-%20A%20Multi-Scenario%20Multi-Task%20Meta%20Learning%20Approach%20for%20Advertiser%20Modeling.pdf)
+- [Mixed Attention Network for Cross-domain Sequential Recommendation](Multi-Scenario/Mixed%20Attention%20Network%20for%20Cross-domain%20Sequential%20Recommendation.pdf)
 - [Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services](Multi-Scenario/Multi-Graph%20based%20Multi-Scenario%20Recommendation%20in%20Large-scale%20Online%20Video%20Services.pdf)
 - [Multi-Scenario Ranking with Adaptive Feature Learning](Multi-Scenario/Multi-Scenario%20Ranking%20with%20Adaptive%20Feature%20Learning.pdf)
 - [Personalized Transfer of User Preferences for Cross-domain Recommendation](Multi-Scenario/Personalized%20Transfer%20of%20User%20Preferences%20for%20Cross-domain%20Recommendation.pdf)