update

Author: tangxyw

CausalInference/Causal Inference in Recommender Systems - A Survey of Strategies for Bias Mitigation, Explanation, and Generalization.pdf

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 # 推荐系统相关论文汇总
 ([English Version is Here](/README_EN.md))
 ## 介绍
-1. 截至2023-01-02，本仓库收集汇总了推荐系统领域相关论文共**543**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2023-01-09，本仓库收集汇总了推荐系统领域相关论文共**547**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)。
@@ -110,6 +110,7 @@
 - [Learning Within-Session Budgets from Browsing Trajectories](Industry/Learning%20Within-Session%20Budgets%20from%20Browsing%20Trajectories.pdf)
 - [Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](Industry/Modeling%20Users%E2%80%99%20Contextualized%20Page-wise%20Feedback%20for%20Click-Through%20Rate%20Prediction%20in%20E-commerce%20Search.pdf)
 - [MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](Industry/MaskNet%20-%20Introducing%20Feature-Wise%20Multiplication%20to%20CTR%20Ranking%20Models%20by%20Instance-Guided%20Mask.pdf)
+- [Micro Behaviors - A New Perspective in E-commerce Recommender Systems](Industry/Micro%20Behaviors%20-%20A%20New%20Perspective%20in%20E-commerce%20Recommender%20Systems.pdf)
 - [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](Industry/Multi-Interactive%20Attention%20Network%20for%20Fine-grained%20Feature%20Learning%20in%20CTR%20Prediction.pdf)
 - [MRIF - Multi-resolution Interest Fusion for Recommendation](Industry/MRIF%20-%20Multi-resolution%20Interest%20Fusion%20for%20Recommendation.pdf)
 - [News Recommendation with Candidate-aware User Modeling](Industry/News%20Recommendation%20with%20Candidate-aware%20User%20Modeling.pdf)
@@ -493,6 +494,7 @@
 - [Privileged Features Distillation at Taobao Recommendations](Distillation/Privileged%20Features%20Distillation%20at%20Taobao%20Recommendations.pdf)
 - [Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net](Distillation/Rocket%20Launching%20-%20A%20Universal%20and%20Efficient%20Framework%20for%20Training%20Well-performing%20Light%20Net.pdf)
 - [Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System](Distillation/Ranking%20Distillation%20-%20Learning%20Compact%20Ranking%20Models%20With%20High%20Performance%20for%20Recommender%20System.pdf)
+- [Unbiased Knowledge Distillation for Recommendation](Distillation/Unbiased%20Knowledge%20Distillation%20for%20Recommendation.pdf)
 ## Feedback-Delay
 - [[2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling](Feedback-Delay/%5B2021%5D%5BAlibaba%5D%20Real%20Negatives%20Matter%20-%20Continuous%20Training%20with%20Real%20Negatives%20for%20Delayed%20Feedback%20Modeling.pdf)
 - [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](Feedback-Delay/Asymptotically%20Unbiased%20Estimation%20for%20Delayed%20Feedback%20Modeling%20via%20Label%20Correction.pdf)
@@ -590,6 +592,7 @@
 - [Two-Stage Audience Expansion for Financial Targeting in Marketing](Look-Alike/Two-Stage%20Audience%20Expansion%20for%20Financial%20Targeting%20in%20Marketing.pdf)
 ## CausalInference
 - [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](CausalInference/A%20Model-Agnostic%20Causal%20Learning%20Framework%20for%20Recommendation%20using%20Search%20Data.pdf)
+- [Causal Inference in Recommender Systems - A Survey of Strategies for Bias  Mitigation, Explanation, and Generalization](CausalInference/Causal%20Inference%20in%20Recommender%20Systems%20-%20A%20Survey%20of%20Strategies%20for%20Bias%20%20Mitigation%2C%20Explanation%2C%20and%20Generalization.pdf)
 - [CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation](CausalInference/CauseRec%20-%20Counterfactual%20User%20Sequence%20Synthesis%20for%20Sequential%20Recommendation.pdf)
 - [Counterfactual Data-Augmented Sequential Recommendation](CausalInference/Counterfactual%20Data-Augmented%20Sequential%20Recommendation.pdf)
 - [Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue](CausalInference/Clicks%20can%20be%20Cheating%20-%20Counterfactual%20Recommendation%20for%20Mitigating%20Clickbait%20Issue.pdf)
@@ -603,6 +606,7 @@
 ## Diversity
 - [[2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems](Diversity/%5B2020%5D%5BHuawei%5D%5BpDPP%5D%20Personalized%20Re-ranking%20for%20Improving%20Diversity%20in%20Live%20Recommender%20Systems.pdf)
 - [A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks](Diversity/A%20Framework%20for%20Recommending%20Accurate%20and%20Diverse%20Items%20Using%20Bayesian%20Graph%20Convolutional%20Neural%20Networks.pdf)
+- [A Survey of Diversification Techniques in Search and Recommendation](Diversity/A%20Survey%20of%20Diversification%20Techniques%20in%20Search%20and%20Recommendation.pdf)
 - [Adaptive, Personalized Diversity for Visual Discovery](Diversity/Adaptive%2C%20Personalized%20Diversity%20for%20Visual%20Discovery.pdf)
 - [Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective](Diversity/Diversity%20on%20the%20Go%21%20Streaming%20Determinantal%20Point%20Processes%20under%20a%20Maximum%20Induced%20Cardinality%20Objective.pdf)
 - [DGCN - Diversified Recommendation with Graph Convolutional Networks](Diversity/DGCN%20-%20Diversified%20Recommendation%20with%20Graph%20Convolutional%20Networks.pdf)

Distillation/Unbiased Knowledge Distillation for Recommendation.pdf

@@ -1,7 +1,7 @@
 
 # Summary of Papers Related to Recommendation System
 ## Introduce
-1. Up to 2023-01-02, **543** papers related to recommendation system have been collected and summarized in this repo, 
+1. Up to 2023-01-09, **547** 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**, **ReinForce Learning** and other fields, the repo will track the industry progress and update continuely.
@@ -117,6 +117,7 @@ I will remove it immediately after verification.
 - [Learning Within-Session Budgets from Browsing Trajectories](Industry/Learning%20Within-Session%20Budgets%20from%20Browsing%20Trajectories.pdf)
 - [Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](Industry/Modeling%20Users%E2%80%99%20Contextualized%20Page-wise%20Feedback%20for%20Click-Through%20Rate%20Prediction%20in%20E-commerce%20Search.pdf)
 - [MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](Industry/MaskNet%20-%20Introducing%20Feature-Wise%20Multiplication%20to%20CTR%20Ranking%20Models%20by%20Instance-Guided%20Mask.pdf)
+- [Micro Behaviors - A New Perspective in E-commerce Recommender Systems](Industry/Micro%20Behaviors%20-%20A%20New%20Perspective%20in%20E-commerce%20Recommender%20Systems.pdf)
 - [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](Industry/Multi-Interactive%20Attention%20Network%20for%20Fine-grained%20Feature%20Learning%20in%20CTR%20Prediction.pdf)
 - [MRIF - Multi-resolution Interest Fusion for Recommendation](Industry/MRIF%20-%20Multi-resolution%20Interest%20Fusion%20for%20Recommendation.pdf)
 - [News Recommendation with Candidate-aware User Modeling](Industry/News%20Recommendation%20with%20Candidate-aware%20User%20Modeling.pdf)
@@ -500,6 +501,7 @@ I will remove it immediately after verification.
 - [Privileged Features Distillation at Taobao Recommendations](Distillation/Privileged%20Features%20Distillation%20at%20Taobao%20Recommendations.pdf)
 - [Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net](Distillation/Rocket%20Launching%20-%20A%20Universal%20and%20Efficient%20Framework%20for%20Training%20Well-performing%20Light%20Net.pdf)
 - [Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System](Distillation/Ranking%20Distillation%20-%20Learning%20Compact%20Ranking%20Models%20With%20High%20Performance%20for%20Recommender%20System.pdf)
+- [Unbiased Knowledge Distillation for Recommendation](Distillation/Unbiased%20Knowledge%20Distillation%20for%20Recommendation.pdf)
 ## Feedback-Delay
 - [[2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling](Feedback-Delay/%5B2021%5D%5BAlibaba%5D%20Real%20Negatives%20Matter%20-%20Continuous%20Training%20with%20Real%20Negatives%20for%20Delayed%20Feedback%20Modeling.pdf)
 - [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](Feedback-Delay/Asymptotically%20Unbiased%20Estimation%20for%20Delayed%20Feedback%20Modeling%20via%20Label%20Correction.pdf)
@@ -597,6 +599,7 @@ I will remove it immediately after verification.
 - [Two-Stage Audience Expansion for Financial Targeting in Marketing](Look-Alike/Two-Stage%20Audience%20Expansion%20for%20Financial%20Targeting%20in%20Marketing.pdf)
 ## CausalInference
 - [A Model-Agnostic Causal Learning Framework for Recommendation using Search Data](CausalInference/A%20Model-Agnostic%20Causal%20Learning%20Framework%20for%20Recommendation%20using%20Search%20Data.pdf)
+- [Causal Inference in Recommender Systems - A Survey of Strategies for Bias  Mitigation, Explanation, and Generalization](CausalInference/Causal%20Inference%20in%20Recommender%20Systems%20-%20A%20Survey%20of%20Strategies%20for%20Bias%20%20Mitigation%2C%20Explanation%2C%20and%20Generalization.pdf)
 - [CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation](CausalInference/CauseRec%20-%20Counterfactual%20User%20Sequence%20Synthesis%20for%20Sequential%20Recommendation.pdf)
 - [Counterfactual Data-Augmented Sequential Recommendation](CausalInference/Counterfactual%20Data-Augmented%20Sequential%20Recommendation.pdf)
 - [Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue](CausalInference/Clicks%20can%20be%20Cheating%20-%20Counterfactual%20Recommendation%20for%20Mitigating%20Clickbait%20Issue.pdf)
@@ -610,6 +613,7 @@ I will remove it immediately after verification.
 ## Diversity
 - [[2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems](Diversity/%5B2020%5D%5BHuawei%5D%5BpDPP%5D%20Personalized%20Re-ranking%20for%20Improving%20Diversity%20in%20Live%20Recommender%20Systems.pdf)
 - [A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks](Diversity/A%20Framework%20for%20Recommending%20Accurate%20and%20Diverse%20Items%20Using%20Bayesian%20Graph%20Convolutional%20Neural%20Networks.pdf)
+- [A Survey of Diversification Techniques in Search and Recommendation](Diversity/A%20Survey%20of%20Diversification%20Techniques%20in%20Search%20and%20Recommendation.pdf)
 - [Adaptive, Personalized Diversity for Visual Discovery](Diversity/Adaptive%2C%20Personalized%20Diversity%20for%20Visual%20Discovery.pdf)
 - [Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective](Diversity/Diversity%20on%20the%20Go%21%20Streaming%20Determinantal%20Point%20Processes%20under%20a%20Maximum%20Induced%20Cardinality%20Objective.pdf)
 - [DGCN - Diversified Recommendation with Graph Convolutional Networks](Diversity/DGCN%20-%20Diversified%20Recommendation%20with%20Graph%20Convolutional%20Networks.pdf)