diff --git a/Embedding/Extend/[MGQE][WWW 20][Google] Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems.pdf b/00 Embedding/Extend/[MGQE][WWW 20][Google] Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems.pdf
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diff --git a/Embedding/Extend/[SSL][Google] Self-supervised Learning for Deep Models in Recommendations.pdf b/00 Embedding/Extend/[SSL][Google] Self-supervised Learning for Deep Models in Recommendations.pdf
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diff --git a/00 Embedding/Note/Word2Vec.md b/00 Embedding/Note/Word2Vec.md
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+# Word2Vec
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/00 Embedding/README.md b/00 Embedding/README.md
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+- [[EFIN][KDD 23][Tencent]Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://arxiv.org/abs/2306.00315)
+- [[SDC][KDD 23][Tencent]Binary Embedding-based Retrieval at Tencent](https://arxiv.org/abs/2302.08714)
+- [[MIPS][KDD 23][Meta]Revisiting Neural Retrieval on Accelerators](https://arxiv.org/abs/2306.04039)
+- [[UnifileR][KDD 23][Microsoft]UnifieR: A Unified Retriever for Large-Scale Retrieval](https://arxiv.org/abs/2205.11194)
diff --git a/00 Embedding/[ALPT][AAAI23][HuaWei] Adaptive Low-Precision Training for Embeddings in Click-Through Rate.pdf b/00 Embedding/[ALPT][AAAI23][HuaWei] Adaptive Low-Precision Training for Embeddings in Click-Through Rate.pdf
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diff --git a/Embedding/[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf b/00 Embedding/[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf
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rename to 00 Embedding/[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf
diff --git a/Embedding/[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf b/00 Embedding/[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf
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rename to 00 Embedding/[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf
diff --git a/Embedding/[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations.pdf b/00 Embedding/[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations.pdf
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rename from Embedding/[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations.pdf
rename to 00 Embedding/[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations.pdf
diff --git a/Embedding/[Etsy Embedding][DAPA-WSDM 19] Learning Item-Interaction Embeddings for User Recommendations.pdf b/00 Embedding/[Etsy Embedding][DAPA-WSDM 19] Learning Item-Interaction Embeddings for User Recommendations.pdf
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rename to 00 Embedding/[Etsy Embedding][DAPA-WSDM 19] Learning Item-Interaction Embeddings for User Recommendations.pdf
diff --git a/Embedding/[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networks.pdf b/00 Embedding/[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networks.pdf
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rename from Embedding/[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networks.pdf
rename to 00 Embedding/[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networks.pdf
diff --git a/Embedding/[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf b/00 Embedding/[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf
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rename from Embedding/[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf
rename to 00 Embedding/[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf
diff --git a/Embedding/[KDD 20][FaceBook] Embedding-based Retrieval in Facebook Search.pdf b/00 Embedding/[KDD 20][FaceBook] Embedding-based Retrieval in Facebook Search.pdf
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rename from Embedding/[KDD 20][FaceBook] Embedding-based Retrieval in Facebook Search.pdf
rename to 00 Embedding/[KDD 20][FaceBook] Embedding-based Retrieval in Facebook Search.pdf
diff --git a/Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf b/00 Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf
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rename from Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf
rename to 00 Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf
diff --git a/Embedding/[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks.pdf b/00 Embedding/[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks.pdf
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rename from Embedding/[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks.pdf
rename to 00 Embedding/[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks.pdf
diff --git a/Embedding/[SDNE][KDD 16] Structural Deep Network Embedding.pdf b/00 Embedding/[SDNE][KDD 16] Structural Deep Network Embedding.pdf
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rename from Embedding/[SDNE][KDD 16] Structural Deep Network Embedding.pdf
rename to 00 Embedding/[SDNE][KDD 16] Structural Deep Network Embedding.pdf
diff --git a/Embedding/[Struc2Vec][KDD 17]struc2vec_Learning Node Representations from Structural Identity.pdf b/00 Embedding/[Struc2Vec][KDD 17]struc2vec_Learning Node Representations from Structural Identity.pdf
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rename to 00 Embedding/[Struc2Vec][KDD 17]struc2vec_Learning Node Representations from Structural Identity.pdf
diff --git a/Embedding/[Word2Vec][2013][Google] Efficient Estimation of Word Representations in Vector Space.pdf b/00 Embedding/[Word2Vec][2013][Google] Efficient Estimation of Word Representations in Vector Space.pdf
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rename from Embedding/[Word2Vec][2013][Google] Efficient Estimation of Word Representations in Vector Space.pdf
rename to 00 Embedding/[Word2Vec][2013][Google] Efficient Estimation of Word Representations in Vector Space.pdf
diff --git a/Embedding/[Word2Vec][NIPS 13][Google] Distributed Representations of Words and Phrases and their Compositionality.pdf b/00 Embedding/[Word2Vec][NIPS 13][Google] Distributed Representations of Words and Phrases and their Compositionality.pdf
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diff --git "a/01 Ranking/Note/AutoFAS Automatic Feature and Architecture Selection for Pre-Ranking System \350\256\272\346\226\207\347\254\224\350\256\260.md" "b/01 Ranking/Note/AutoFAS Automatic Feature and Architecture Selection for Pre-Ranking System \350\256\272\346\226\207\347\254\224\350\256\260.md"
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+# AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System 论文笔记
+
+[论文](https://arxiv.org/abs/2205.09394)
+
+## Motivation
+
+##### 先前的方法没有明确地对性能收益与计算开销进行建模，在预排序阶段的延迟约束会导致次优的结果；从教师模型中迁移知识到预先定义好结构的学生模型中也会对模型的结果造成影响。
+
+##### AutoFAS第一次同时选择最有价值的特征和使用神经结构搜索选择网络结构，其能够在排序教师模型的帮助下选择最合适的预排序结构。
+
+![image-20230213201512193](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213201512193.png)
+
+## Model
+
+![image-20230213212911390](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213212911390.png)
+
+##### 其模型结构主要包含两部分，左边的排序教师模型和右边的预排序学生模型，左边的教师模型主要用于特征的选择以及表征的学习，预排序网络结构中主要是多种不同的多层感知机，感知机的输入结构不一样，通过结构搜索技术为该学生模型选择合适的架构，避免了人工搜索带来的问题。其算法流程如下所示：输入相关的数据，先训练一个排序模型，然后使用训练好的排序模型作为教师模型，然后训练网络更新Mask和L参数，在每一个层中选择最重要的特征和结构，然后使用知识蒸馏训练该选中的结构，输出预先排序模型
+
+![image-20230213214002972](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213214002972.png)
+
+## Performance
+
+##### 从实验结果可以看出使用该方法不仅提高了精度，还大大减少了内存消耗和时间延迟，从表4可以看出其提升了精度，并且其资源消耗以及时间延迟较与baseline都是可以比较的
+
+![image-20230213214043421](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213214043421.png)
+
+![image-20230213214156777](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213214156777.png)
+
+## Ablation Study
+
+##### 从实验结果看出其每一个板块都是有用的，并且在增大学生网络参数的时候其结构化搜索会使得模型消耗更少的时间
+
+![image-20230213214216508](C:\Users\QinHsiu\AppData\Roaming\Typora\typora-user-images\image-20230213214216508.png)
+
+## Conclusion
+
+##### 这篇工作提出了一种端到端的自动化机器学习预排序模型。与简单地考虑特征的连接不同的是，该模型同时地选择特征和模型结构，联合优化使得其在计算开销和表现上都取得不错的效果，另外使用知识蒸馏技术从教师模型中学习有用的知识用于预排序。
+
+## References
+
diff --git a/01 Ranking/README.md b/01 Ranking/README.md
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+![avator](rank.png)
+![avator](rank_1.png)
+- PreRanking
+    - [[AutoFAS][KDD 22][Mei tuan]AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System](https://arxiv.org/abs/2205.09394)
+- [[ATRank][AAAI 18][Alibaba]ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation](https://arxiv.org/abs/1711.06632)
+- [[RankFormer][KDD 23][Amazon]RankFormer: Listwise Learning-to-Rank Using Listwide Labels](https://arxiv.org/abs/2306.05808)
+- [[MOO][KDD 23][Amazon]Querywise Fair Learning to Rank through Multi-Objective Optimization](https://www.amazon.science/publications/querywise-fair-learning-to-rank-through-multi-objective-optimization)
+- [[MLLTR][KDD 23][Amazon]Multi-Label Learning to Rank through Multi-Objective Optimization](https://arxiv.org/abs/2207.03060)
+- [[ULTR][KDD 23][Google]Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://arxiv.org/abs/2212.13937)
+
diff --git a/Ranking/[ACL 19][Microsoft] Neural News Recommendation with Long- and Short-term User Representations.pdf b/01 Ranking/[ACL 19][Microsoft] Neural News Recommendation with Long- and Short-term User Representations.pdf
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diff --git a/Ranking/[BERT4Rec][CIKM 19][Alibaba] BERT4Rec_Sequential Recommendation with Bidirectional Encoder Representations from Transformer.pdf b/01 Ranking/[BERT4Rec][CIKM 19][Alibaba] BERT4Rec_Sequential Recommendation with Bidirectional Encoder Representations from Transformer.pdf
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diff --git a/Ranking/[BST][DLP-KDD 19][Alibaba] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba.pdf b/01 Ranking/[BST][DLP-KDD 19][Alibaba] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba.pdf
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diff --git a/01 Ranking/[COLD][KDD 20][Alibaba] COLD Towards the Next Generation of Pre-Ranking System.pdf b/01 Ranking/[COLD][KDD 20][Alibaba] COLD Towards the Next Generation of Pre-Ranking System.pdf
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diff --git a/Ranking/[Choppy][SIGIR 20][Google] Choppy- Cut Transformer For Ranked List Truncation.pdf b/01 Ranking/[Choppy][SIGIR 20][Google] Choppy- Cut Transformer For Ranked List Truncation.pdf
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diff --git a/Ranking/[FTRL][KDD 13][Google] Ad Click Prediction_a View from the Trenches.pdf b/01 Ranking/[FTRL][KDD 13][Google] Ad Click Prediction_a View from the Trenches.pdf
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diff --git a/Ranking/[GBDT+LR][ADKDD 14][Facebook] Practical Lessons from Predicting Clicks on Ads at Facebook.pdf b/01 Ranking/[GBDT+LR][ADKDD 14][Facebook] Practical Lessons from Predicting Clicks on Ads at Facebook.pdf
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diff --git a/Ranking/[ImageCTR][CIKM 18][Alibaba] Image Matters_Visually modeling user behaviors using Advanced Model Server.pdf b/01 Ranking/[ImageCTR][CIKM 18][Alibaba] Image Matters_Visually modeling user behaviors using Advanced Model Server.pdf
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diff --git a/Ranking/[MDM 19] Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks.pdf b/01 Ranking/[MDM 19] Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks.pdf
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diff --git a/01 Ranking/[MEiTuan][KDD 22][MeiTuan] AutoFAS Automatic Feature and Architecture Selection for.pdf b/01 Ranking/[MEiTuan][KDD 22][MeiTuan] AutoFAS Automatic Feature and Architecture Selection for.pdf
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diff --git a/Ranking/[arxiv 18][Facebook] Collaborative Multi-modal Deep Learning for The Personalized Product Retrieval in Facebook Marketplace.pdf b/01 Ranking/[arxiv 18][Facebook] Collaborative Multi-modal Deep Learning for The Personalized Product Retrieval in Facebook Marketplace.pdf
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diff --git a/01 Ranking/rank.png b/01 Ranking/rank.png
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diff --git a/02 ReRank/README.md b/02 ReRank/README.md
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+- [[AutoIntent][SIGIR 22][Mei tuan]Automatically Discovering User Consumption Intents in Meituan](https://dl.acm.org/doi/pdf/10.1145/3534678.3539122)
+- [[CMR][KDD 23][Alibaba]Controllable Multi-Objective Re-ranking with Policy Hypernetworks](https://arxiv.org/abs/2306.05118)
+- [[MIREC][KDD 23][Alibaba]Multi-channel Integrated Recommendation with Exposure Constraints](https://arxiv.org/abs/2305.12319)
+- [[MPAD][KDD 23][Alibaba]Multi-factor Sequential Re-ranking with Perception-Aware Diversification](https://arxiv.org/abs/2305.12420)
+- [[KDD 23][Hua Wei]On-device Integrated Re-ranking with Heterogeneous Behavior Modeling](https://www.youtube.com/watch?v=1UirlORuWgo)
+- [[PIER][KDD 23][Mei Tuan]PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://arxiv.org/abs/2302.03487)
diff --git a/ReRank/[PRM][RecSys 19][Alibaba] Personalized Re-ranking for Recommendation.pdf b/02 ReRank/[PRM][RecSys 19][Alibaba] Personalized Re-ranking for Recommendation.pdf
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diff --git a/ReRank/[SIGIR 18] Learning a Deep Listwise Context Model for Ranking Refinement.pdf b/02 ReRank/[SIGIR 18] Learning a Deep Listwise Context Model for Ranking Refinement.pdf
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diff --git a/CTR/Extend/CAN_Revisiting Feature Co-Action for Click-Through Rate Prediction.pdf b/03 CTR/Extend/CAN_Revisiting Feature Co-Action for Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/DCN-M_Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.pdf b/03 CTR/Extend/DCN-M_Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.pdf
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diff --git a/CTR/Extend/FuxiCTR_An Open Benchmark for Click-Through Rate Prediction.pdf b/03 CTR/Extend/FuxiCTR_An Open Benchmark for Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/[AAAI 20][Alibaba] Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.pdf b/03 CTR/Extend/[AAAI 20][Alibaba] Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.pdf
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diff --git a/CTR/Extend/[AAAI 20][Alibaba] Rocket Launching_A Universal and Efficient Framework for Training Well-performing Light Net.pdf b/03 CTR/Extend/[AAAI 20][Alibaba] Rocket Launching_A Universal and Efficient Framework for Training Well-performing Light Net.pdf
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diff --git a/CTR/Extend/[Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction.pdf b/03 CTR/Extend/[Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/[AutoFIS][KDD 20][Huawei] AutoFIS_Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction.pdf b/03 CTR/Extend/[AutoFIS][KDD 20][Huawei] AutoFIS_Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/[DMR][AAAI 20][Alibaba] Deep Match to Rank Model for Personalized Click-Through Rate Prediction.pdf b/03 CTR/Extend/[DMR][AAAI 20][Alibaba] Deep Match to Rank Model for Personalized Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/[KDD 19][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.pdf b/03 CTR/Extend/[KDD 19][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.pdf
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diff --git a/CTR/Extend/[MARN][WWW 20][Alibaba] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction.pdf b/03 CTR/Extend/[MARN][WWW 20][Alibaba] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction.pdf
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diff --git "a/CTR/Extend/[MOBUIS][KDD 19] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu\342\200\231s Sponsored Search.pdf" "b/03 CTR/Extend/[MOBUIS][KDD 19] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu\342\200\231s Sponsored Search.pdf"
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diff --git a/CTR/Extend/[PAL][RecSys 19][Huawei] PAL_A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems.pdf b/03 CTR/Extend/[PAL][RecSys 19][Huawei] PAL_A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems.pdf
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rename to 03 CTR/Extend/[PAL][RecSys 19][Huawei] PAL_A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems.pdf
diff --git a/CTR/Extend/[PQR][AAAI 20][Tencent] Projective Quadratic Regression for Online Learning.pdf b/03 CTR/Extend/[PQR][AAAI 20][Tencent] Projective Quadratic Regression for Online Learning.pdf
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diff --git "a/03 CTR/Extend/[RACP][WSDM 22][Alibaba] Modeling Users\342\200\231 Contextualized Page-wise Feedback.pdf" "b/03 CTR/Extend/[RACP][WSDM 22][Alibaba] Modeling Users\342\200\231 Contextualized Page-wise Feedback.pdf"
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diff --git a/CTR/Extend/[SIGIR 20][Alibaba] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction.pdf b/03 CTR/Extend/[SIGIR 20][Alibaba] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction.pdf
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diff --git a/03 CTR/Extend/[TGIN][WSDM 22][Alibaba] Triangle Graph Interest Network for Click-through.pdf b/03 CTR/Extend/[TGIN][WSDM 22][Alibaba] Triangle Graph Interest Network for Click-through.pdf
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diff --git a/CTR/Extend/[WWW 20][Tencent]Field-aware Calibration_A Simple and Empirically Strong Method for Reliable Probabilistic Predictions.pdf b/03 CTR/Extend/[WWW 20][Tencent]Field-aware Calibration_A Simple and Empirically Strong Method for Reliable Probabilistic Predictions.pdf
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diff --git a/03 CTR/Note/DCN.md b/03 CTR/Note/DCN.md
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+++ b/03 CTR/Note/DCN.md	
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+# DCN
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/03 CTR/Note/DIN.md b/03 CTR/Note/DIN.md
new file mode 100644
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+++ b/03 CTR/Note/DIN.md	
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+# DIN
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/03 CTR/Note/DeepCrossing.md b/03 CTR/Note/DeepCrossing.md
new file mode 100644
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+++ b/03 CTR/Note/DeepCrossing.md	
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+# DeepCrossing
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/03 CTR/Note/DeepFM.md b/03 CTR/Note/DeepFM.md
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+++ b/03 CTR/Note/DeepFM.md	
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+# DeepFM
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/03 CTR/Note/FactorizationMachine.md b/03 CTR/Note/FactorizationMachine.md
new file mode 100644
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+++ b/03 CTR/Note/FactorizationMachine.md	
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+# 因子分解机做推荐预测任务
+
+### 1.因子分解的含义
+
+##### 分解机中的分解是来自于其在求解特征交叉项系数的时候用到了矩阵分解，其在逻辑回归的基础上引入了一个特征交叉项，也即考虑了不同特征之间的关系，因为数据系数的原因直接通过观测值来代表特征交叉项的系数会导致大部分数据因为系数（某些特征交叉项系数为0）为0而无法发挥作用；
+
+### 2.因子分解机表达式，以二阶特征交叉为例
+
+### $$线性回归的表达式为:\hat{y}=\omega_{0}+\sum^{n}\limits_{i=1}\omega_{i}x_{i}$$
+
+### $$FM二阶特征交叉表达式为:\hat{y}=\omega_{0}+\sum\limits_{i=1}^{n}\omega_{i}x_{i}+\sum^{n-1}\limits_{i=1}\sum^{n}\limits_{j=i+1}\omega_{ij}x_{i}x_{j}$$
+
+##### 相较于之前的线性回归方法，FM考虑了不同特征之间的交叉关系，现在最关键的一点就是如何求解交叉特征的系数，最简单的方法是依据观测变量得出，但是这会面临数据系数的问题，最合理的方法是通过模型学习来获得;
+
+### $$\omega_{ij}=\textless v_{i},v_{j}\textgreater=v_{i}\cdot v_{j},v_{i}=(v_{i1},v_{i2},...,v_{ik})^{T} \in \mathbb{R}^{k},i=1,2,..,n$$
+
+### $$w_{ij}=v_{i}^{T}v_{j}=\sum^{k}\limits_{i=1}v_{ik}v_{jk},该表达式对应矩阵分解方法,因此模型的方法称之为因子分解机$$
+
+### $$其满足当k足够大的时候,对于任意对称正定实矩阵\hat{\omega}\in\mathbb{R}^{n\times n},均存在一个实矩阵v\in\mathbb{R}^{n\times k},使得\hat{\omega}=vv^{T}.因此vv^{T}的表达能力足够强$$
+
+### 3.因子分解机一般步骤
+
+- 数据处理与LR一致，将特征转换为数值型特征
+- 结合业务构建FM模型
+- 通过梯度下降算法更新模型参数
+- 更具模型输出结果对商品进行排序，返回推荐列表
+
+### 4.因子分解机的优缺点
+
+- 优点
+  - 引入隐向量可以很好地缓解数据稀疏带来的问题，使用该方法可以使得模型学习到不同特征交叉关系，并且使用隐向量，给定两个特征A和B,当A和B没有交互的时候，可以依据AC更新A，依据BD更新B，大幅度就降低了模型对数据稀疏性的要求；
+  - 降低了最原始特征交叉的空间复杂度，最原始两两特征交叉需要O(n^2)，n表示特征数目，现在只需要nk(k远远小于n)个；
+  - 其在一定程度上丢失了某些特征组合的精确记忆能力（例如有的特征交叉频繁，使用暴力特征交叉可以学到这一点，但是加入隐向量之后一些重要的交叉特征就变得平凡了），但是其大大提高了模型的泛化性能；
+- 缺点
+  - 在考虑高阶（三阶及以上）特征交叉的时候，会面临梯度爆炸的问题，降低了模型的学习能力
+
+### 5.因子分解机后的改进版本
+
+- FFM
+  - 在FM的基础上引入特征域，其隐向量由原来的一维变成了多维，也就是说每一个特征对应的不是唯一一个隐向量，而是一组隐向量，给定三个特征A，B，C，在FM中每一个特征对应一个隐向量，分别为va,vb,vc，所以A与B的交叉特征的权重为(va,vb)，A与C的交叉特征的权重为(va,vc)，这里两个权重中使用的va是同一个隐向量；在FFM中会为每一个特征分配f-1个隐向量，f表示特征的数目，也即同样情况下A的隐向量表示为{vab,vac}，B的隐向量表示为{vba,vbc}，C的隐向量为{vca,vcb}，A与B的交叉特征的权重为(vab,vba)，A与C的交叉特征的权重为(vac,vca)，这样使得计算两个权重的时候不会互相干扰，增强了模型的表达和泛化能力；
+  
+  - ### $$\hat{y}=\omega_{0}+\sum\limits_{i=1}^{n}\omega_{i}x_{i}+\sum^{n-1}\limits_{i=1}\sum^{n}\limits_{j=i+1}(\omega_{j1,f2}\cdot\omega_{j2,f1})x_{i}x_{j}$$
+  
+  - 其参数数量可以表示为nxfxk，其中n表示特征数目，f表示特征域（一般为特征数目-1），k表示隐向量维度，这里其计算复杂度为O(kn^2)，其复杂度较于FM有显著的提升，但是其泛化能力有所提高；其和FM一样只适用于二阶特征交叉，三阶会导致梯度爆炸的问题；
+
+### 6.python实现
+
+### 7.参考资料
+
+[1] 
+
+[2] 王喆《深度学习推荐系统》
+
+[3] [分解机推荐算法原理](https://www.cnblogs.com/pinard/p/6370127.html)
+
diff --git a/03 CTR/Note/LogisticRegression.md b/03 CTR/Note/LogisticRegression.md
new file mode 100644
index 0000000..747566e
--- /dev/null
+++ b/03 CTR/Note/LogisticRegression.md	
@@ -0,0 +1,105 @@
+# 逻辑回归做推荐预测任务
+
+### 1.逻辑回归的含义
+
+##### 逻辑回归是线性回归的衍生，线性回归的输出为预测值，逻辑回归的输出为转换后的概率值，一般使用sigmod函数求解，其中逻辑logit是log odds对数几率的意思，几率表示的是正样本的相对可能性；
+
+### 2.逻辑回归的表达式以及求导
+
+### $$线性回归的表达式:y=\omega x+b$$
+
+### $$y=\sigma(\omega^{T} x+b)=\frac{1}{1+\exp(-\omega^{T} x-b)},y表示模型输出的预测概率,\ln\frac{y}{1-y}=\omega^{T}x+b,\frac{y}{1-y}表示几率，其含义是预测x为正例的概率的相对可能性,\ln(\frac{y}{1-y})表示对数几率，也即logit$$
+
+##### 给定一个二分类任务y={0,1}，假设就y的后验概率估计分别为p(y=1|x)和p(y=0|x)，则对应的表达式可以改写为：
+
+### $$\ln\frac{p(y=1|x)}{p(y=0|x)}=\omega^{T}x+b,p(y=1|x)=\frac{\exp(\omega^{T}x+b)}{1+\exp(\omega^{T}x+b)},p(y=0|x)=\frac{1}{1+\exp(\omega^{T}x+b)}$$
+
+##### 优化目标是为了让模型尽可能预测出正确的类别，可以通过极大似然来估计对应的omega和bias，求解步骤如下所示：
+
+### $$假设\beta=(\omega;b),\hat{x}=(x;1),\beta^{T}\hat{x}=\omega^{T}x+b,再令p_{1}(y=1|x)=p_{1}(y=1|\hat{x};\beta)=\frac{\exp(\beta^{T}\hat{x})}{1+\beta^{T}\hat{x}},p_{0}(y=0|x)=1-p_{1}(y=1|\hat{x};\beta)=\frac{1}{1+\exp(\beta^{T}\hat{x})}$$
+
+### $$优化目标为最大化对数似然,\mathcal{l}(\omega,b)=\sum\limits^{m}_{i=1}\ln p(y_{i}|\hat{x};\beta)$$
+
+### $$极大似然可以重写为:p(y_{i}|x_{i};\beta)=y_{i}p_{1}(\hat{x};\beta)+(1-y_{i})p_{0}(\hat{x};\beta))$$
+
+### $$综合上面两个式子,最大化对数似然相当于最小化\mathcal{l}(\beta)=\sum^{m}\limits_{i=1}(-y_{i}\beta^{T}\hat{x}+\ln(1+\exp(\beta^{T}\hat{x})))$$
+
+### $$其中\beta为高阶可导连续凸函数,由凸优化理论可知^{2},其最优解可以由梯度下降算法和牛顿法获得,\beta^{\star}=\arg\max\limits_{\beta}(\mathcal{l}(\beta)),其牛顿法迭代公式如下所示:$$
+
+### $$牛顿法:\beta^{t+1}=\beta^{t}-(\frac{\partial^{2}\mathcal{l(\beta)}}{\partial \beta \partial \beta^{T}})^{-1}\frac{\partial \mathcal{l}(\beta)}{\partial \beta},一阶导数:\frac{\partial\mathcal{l(\beta)}}{\partial \beta}=-\sum^{m}\limits_{i=1}\hat{x_{i}}(y_{i}-p_{1}(\hat{x_{i}};\beta)),二阶导数:\frac{\partial^{2}\mathcal{l(\beta)}}{\partial \beta \partial \beta^{T}}=\sum^{m}\limits_{i=1}\hat{x_{i}}\hat{x}_{i}^{T}p_{1}(\hat{x_{i}};\beta)(1-p_{1}(\hat{x_{i}};\beta))$$
+
+### $$梯度下降法:\beta^{t+1}=\beta^{t}-\gamma\frac{\partial \mathcal{l}(\beta)}{\partial\beta}$$
+
+### 3.逻辑回归的一般步骤
+
+- 构建和处理训练数据，将所有特征转换为数值型特征向量
+- 确立逻辑回归模型的优化目标，建立逻辑回归模型（确定是二分类还是多分类模型）
+- 模型训练，优化和更新模型参数
+- 对测试集做出相关预测，并根据预测结果对商品进行排序，返回推荐列表
+
+### 4.逻辑回归的优缺点
+
+- 优点
+  - 有数学支撑，其假设因变量y服从伯努利分布（0-1分布，n重二项式分布），线性模型假设y服从高斯分布，明显不适用于分类问题，可解释性强，其为每一个特征分配不同的权重，考虑了预测过程中不同特征的重要性不同
+  - 实现简单，分类时计算量小，速度快，存储资源消耗低，易于理解和实现，广泛应用于工业问题
+  - 便于观察样本分类概率分数
+  - 可以结合L2正则化技术来缓解多重共线性问题
+
+- 缺点
+  - 特征空间较大的时候其性能会有所下降，难以处理大量多类特征或变量
+  - 表达能力差，无法进行特征交叉、特征筛选等一系列更具可解释性的操作，因而造成信息的损失，容易欠拟合，其准确度一般不高
+  - 大多处于二分类问题，使用softmax可以处理多分类，但主要用于线性可分的情况
+  - 对于非线性特征，需要进行特征转换
+
+### 5.LR的演化版本
+
+- GBDT+LR
+  - GBDT是由多棵回归树组成的树林，后一棵树以前面树林的结果与目标结果的残差作为拟合目标，例如当前有三棵树，其拟合结果为T3(x)=t1(x)+t2(x)+t3(x)，目标值为F(x)，则当前的残差为R(x)=F(x)-T3(x)，第四棵树的拟合目标即为R(x)，其每一棵树的生成过程是一棵树的标准的回归树生成过程，因此其回归树中每一个节点的分裂是一个自然的特征选择的过程，而多层节点的结构则对特征进行了有效的自动组合，高效地简化了特征选择和特征组合所带来的繁琐问题；
+  - 该方法分两步进行，第一步使用GBDT进行特征组合，第二步使用LR进行预估；
+  - 优点
+    - 特征工程模型化，减少了复杂的人力劳动（对数据的处理，对模型中特征交叉的设计等）；
+    - 决策树的深度决定了特征交叉的阶数，使用三层就可以完成特征的三阶交叉，其缓解了FM不能进行高阶特征交叉的问题，大大提升了模型的泛化性能；
+  - 缺点
+    - GBDT容易发生过拟合，另外使用该方法会丢失大量特征的数值信息；
+    - 无法完全进行并行训练，更新参数所需的训练时间较长；
+
+- LS-PLM;MLR
+
+  - 其在逻辑回归的基础上引入分治的思想，其分两步骤进行，第一步先对全量数据进行聚类，第二步对每一个类别单独进行逻辑回归预测；
+
+  - ### $$y=\sum^{m}\limits_{i=1}\pi_{i}(x)\cdot\eta_{i}(x)=\sum^{m}\limits_{i=1}softmax(x)\sigma(x)=\sum^{m}\limits_{i=1}\frac{\exp(\mu_{i}\cdot x)}{\sum^{m}\limits_{j=1}\exp(\mu_{j}\cdot x)}\cdot\frac{1}{1+\exp(-\omega_{i} x)}$$
+
+  - 其中m表示聚类的数目，m越大其分的粒度越细，可以较好地平衡模型的拟合和推广能力，m=1时其退化为简单的逻辑回归模型；另外其模型参数规模也随m的增大而线性增长，模型收敛所需的训练样本也随之增长；
+
+  - 优点
+
+    - 端到端的非线性的拟合能力：其具有样本分片的能力，可以挖掘数据中的非线性模式，省去了大量的人工处理样本和特征工程的过程，使得该算法可以端到端的进行训练，便于用一个全局模型对不同应用领域、业务场景进行统一建模；
+    - 模型的稀疏性强：其在建模过程中引入了L1和L2范数，可以使得最终训练出来的模型具有较高的稀疏度，使得模型的部署更加轻量级；模型服务过程中仅需使用权重非0的特征，因此稀疏模型也使其在线推断效率更高；
+    - 模型架构类似于三层神经网络，具备较强的表达能力；
+
+  - 缺点
+
+    - 模型结构相对简单，有进一步提高的空间
+
+### 6.python实现
+
+- 使用sklearn自带的包，使用案例：[LogisticRegression](https://github.com/QinHsiu/DataScience_Basic/tree/main/02 Sklearn)
+
+  ```python
+  from sklearn.linear_model import LogisticRegression
+  ```
+
+- 使用python实现，代码链接：[Logistic_Regression](https://github.com/QinHsiu/DataScience_Basic/tree/main/03 Maching_Learning_in_Action/chapter 05-Logistic_Regression)
+
+### 7.参考资料
+
+[1] 周志华《机器学习》
+
+[2] [Algorithms for Convex Optimization](https://convex-optimization.github.io/)
+
+[3] 王喆《深度学习推荐系统》
+
+
+
+
+
diff --git a/03 CTR/Note/PNN.md b/03 CTR/Note/PNN.md
new file mode 100644
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--- /dev/null
+++ b/03 CTR/Note/PNN.md	
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+# PNN
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/03 CTR/Note/README.md b/03 CTR/Note/README.md
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+
diff --git a/03 CTR/Note/Wide_and_Deep.md b/03 CTR/Note/Wide_and_Deep.md
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+# Wide and Deep做CTR预估
+
+## 1.Wide与Deep的含义
+
+Wide与Deep分别表示模型的两个部分，两者的含义如下所示：
+
+-  Wide部分，一个逻辑回归模型，直接对一些数值型特征做LR回归计算
+- Deep部分，一个深度神经网络模型，使用
+
+## 2.Wide and Deep表达式
+
+### $$y=W^{T}x+b$$
+
+### $$$$
+
+## 3.Wide and Deep一般步骤
+
+- 
+
+## 4.Wide and Deep的优点与缺点
+
+- 优点
+  - 减少了对人工特征的依赖，使用深度学习模型自动学习特征交叉，提高了模型的泛化能力
+- 缺点
+  - 使用Embedding方式对特征进行降维，在面对数据长尾分布的情况时候会导致一些特征无法被充分学习，也有可能无法学习到一些稀疏的高阶特征，从而导致模型过度泛化
+
+## 5.Wide  and Deep后续改进版本
+
+- 
+
+## 6.python实现
+
+## 7.参考资料
+
+[1]
+
diff --git a/03 CTR/README.md b/03 CTR/README.md
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+
+- [[PCFGNN][SIGIR 21][Alibaba]Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction](https://arxiv.org/abs/2105.07752)
+- [[TGIN][WSDM 22][Alibaba]Triangle Graph Interest Network for Click-through Rate Prediction](https://arxiv.org/abs/2202.02698)
+- [[RACP][WSDM 22][Alibaba]Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://arxiv.org/abs/2203.15542)
+- [[HCCM][SIGIR 22][Mei tuan]Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling](https://arxiv.org/abs/2205.02711)
+- [FinalMLP-AAAI23-HuaWei-FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902) [code](https://github.com/xue-pai/FuxiCTR)
+- [[TSCAC][WWW 23][Kuai shou]Two-Stage Constrained Actor-Critic for Short Video Recommendation](https://arxiv.org/abs/2302.01680)
+- [[DBPMaN][WWW 23][Mei tuan]A Deep Behavior Path Matching Network for Click-Through Rate Prediction](https://arxiv.org/abs/2302.00302)
+- [[AutoDenoise][WWW 23]AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://arxiv.org/abs/2303.06611)
+- [[FAN][DASFAA 23][Alibaba]FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation](https://arxiv.org/abs/2304.04529)
+- [[Overfitting][CIKM 22][Alibaba]Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models](https://arxiv.org/abs/2209.06053)
+- [[JRC][KDD 23][Alibaba]Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://arxiv.org/abs/2208.06164)
+- [[ContentCTR][CVPR 23][Kuai Shou]ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer](https://arxiv.org/pdf/2306.14392.pdf)
+- [[KDD 23][Tencent]Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://www.youtube.com/watch?v=4HDJ_fBrso4)
+- [[BERT4CTR][KDD 23][Microsoft]BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction](https://www.youtube.com/watch?v=26xYd3QDYEQ)
+- [[TWIN][KDD 23][Kuai Shou]TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou](https://arxiv.org/abs/2302.02352)
+- [[KDD 23][Tencent]Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://www.youtube.com/watch?v=4HDJ_fBrso4)
+- [[DCIN][CIKM 23][MeiTuan]Deep Context Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/2308.06037.pdf)
+- [[CELS][KDD 23]Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction](http://home.ustc.edu.cn/~yrunl/Files/Publications/KDD23-CELS.pdf)
+- [[MAP][KDD 23][Hua Wei]MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://arxiv.org/pdf/2308.01737.pdf) [Note](https://mp.weixin.qq.com/s/VZggjOckJcwaRP0VbxSG2A)
+- [[AT4CTR][AAAI 24][MeiTuan]AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction](https://arxiv.org/pdf/2312.06683.pdf) [[Note]](https://mp.weixin.qq.com/s/FfwUL08Eng6_eQVpadfLqw)
+- [[UniCTR][Arxiv2023][HuaWei]A Unified Framework for Multi-Domain CTR Prediction via Large Language Models](https://arxiv.org/pdf/2312.10743.pdf) [[Note]](https://mp.weixin.qq.com/s/TSTyAg5U9fqa5e_IzyesMg)
+
+- CVR
+  - [[CL4CVR][KDD 23][Alibaba]Contrastive Learning for Conversion Rate Prediction](https://arxiv.org/pdf/2307.05974.pdf)
+  - [[HDR][KDD 23][Alibaba]Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach](https://arxiv.org/abs/2305.12837)
diff --git a/CTR/[AFM][IJCAI 17] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks.pdf b/03 CTR/[AFM][IJCAI 17] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks.pdf
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diff --git a/CTR/[AutoInt][CIKM 19] AutoInt_Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf b/03 CTR/[AutoInt][CIKM 19] AutoInt_Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf
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diff --git a/03 CTR/[DBPMaN][WWW 23][MeiTuan] A Deep Behavior Path Matching Network for Click-Through.pdf b/03 CTR/[DBPMaN][WWW 23][MeiTuan] A Deep Behavior Path Matching Network for Click-Through.pdf
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diff --git a/CTR/[DCN][KDD 17][Google] Deep & Cross Network for Ad Click Predictions.pdf b/03 CTR/[DCN][KDD 17][Google] Deep & Cross Network for Ad Click Predictions.pdf
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diff --git a/03 CTR/[DFN][IJCAI 20][Tencent] Adaptive User Modeling with Long and Short-Term Preferences for Personalized.pdf b/03 CTR/[DFN][IJCAI 20][Tencent] Adaptive User Modeling with Long and Short-Term Preferences for Personalized.pdf
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diff --git a/CTR/[DIN][KDD 18][Alibaba] Deep Interest Network for Click-Through Rate Prediction.pdf b/03 CTR/[DIN][KDD 18][Alibaba] Deep Interest Network for Click-Through Rate Prediction.pdf
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diff --git a/03 CTR/[DMT][CIKM 20][Jingdong] Deep Multifaceted Transformers for Multi-objective Ranking in.pdf b/03 CTR/[DMT][CIKM 20][Jingdong] Deep Multifaceted Transformers for Multi-objective Ranking in.pdf
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diff --git a/03 CTR/[DSTN][KDD 19][Alibaba] Deep Spatio-Temporal Neural Networks for Click-Through Rate.pdf b/03 CTR/[DSTN][KDD 19][Alibaba] Deep Spatio-Temporal Neural Networks for Click-Through Rate.pdf
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diff --git a/03 CTR/[DiSENCTR][SIGIR 22][MeiTuan] DisenCTR Dynamic Graph-based Disentangled Representation.pdf b/03 CTR/[DiSENCTR][SIGIR 22][MeiTuan] DisenCTR Dynamic Graph-based Disentangled Representation.pdf
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diff --git a/03 CTR/[FM][ICDM2010]Factorization Machines.pdf b/03 CTR/[FM][ICDM2010]Factorization Machines.pdf
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diff --git a/CTR/[FNN][ECIR 16] Deep Learning over Multi-field Categorical Data_A Case Study on User Response Prediction.pdf b/03 CTR/[FNN][ECIR 16] Deep Learning over Multi-field Categorical Data_A Case Study on User Response Prediction.pdf
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diff --git a/CTR/[FiBiNET][RecSys 19][Weibo] FiBiNET_Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf b/03 CTR/[FiBiNET][RecSys 19][Weibo] FiBiNET_Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf
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diff --git a/03 CTR/[FibiNet++][WWW 22][Weibo] FiBiNet++Improving FiBiNet by Greatly Reducing.pdf b/03 CTR/[FibiNet++][WWW 22][Weibo] FiBiNet++Improving FiBiNet by Greatly Reducing.pdf
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diff --git a/03 CTR/[KFAtt][NeurIPS 20][Jingdong] Kalman Filtering Attention for User Behavior.pdf b/03 CTR/[KFAtt][NeurIPS 20][Jingdong] Kalman Filtering Attention for User Behavior.pdf
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diff --git a/CTR/[NFM][SIGIR 17] Neural Factorization Machines for Sparse Predictive Analytics.pdf b/03 CTR/[NFM][SIGIR 17] Neural Factorization Machines for Sparse Predictive Analytics.pdf
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diff --git a/03 CTR/[POEM][KDD 22][MeiTuan] Modeling Persuasion Factor of User Decision for.pdf b/03 CTR/[POEM][KDD 22][MeiTuan] Modeling Persuasion Factor of User Decision for.pdf
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diff --git a/03 CTR/[TWIN][arxiv 23][KuaiShou] TWIN TWo-stage Interest Network for Lifelong.pdf b/03 CTR/[TWIN][arxiv 23][KuaiShou] TWIN TWo-stage Interest Network for Lifelong.pdf
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diff --git a/Match/Extend/Next Item Recommendation with Self-Attention.pdf b/04 Match/Extend/Next Item Recommendation with Self-Attention.pdf
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diff --git a/Match/Extend/[MOBIUS][KDD 19][Baidu] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search.pdf b/04 Match/Extend/[MOBIUS][KDD 19][Baidu] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search.pdf
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diff --git a/Match/[DR][ByteDance] Deep Retrieval_An End-to-End Learnable Structure Model for Large-Scale Recommendations.pdf b/04 Match/[DR][ByteDance] Deep Retrieval_An End-to-End Learnable Structure Model for Large-Scale Recommendations.pdf
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diff --git a/Match/[MIND][arxiv 19][Alibaba] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.pdf b/04 Match/[MIND][arxiv 19][Alibaba] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.pdf
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diff --git a/04 Match/[SCL][arxiv 21][Google] Self-supervised Learning for Large-scale Item Recommendations.pdf b/04 Match/[SCL][arxiv 21][Google] Self-supervised Learning for Large-scale Item Recommendations.pdf
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diff --git a/Match/[TDM][KDD 18][Alibaba] Learning Tree-based Deep Model for Recommender Systems.pdf b/04 Match/[TDM][KDD 18][Alibaba] Learning Tree-based Deep Model for Recommender Systems.pdf
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diff --git a/04 Match/[XMRec][WSDM 22][JingDong] Recommendation as Language Processing (RLP).pdf b/04 Match/[XMRec][WSDM 22][JingDong] Recommendation as Language Processing (RLP).pdf
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diff --git a/Match/deep learning for matching in search and recommendation book 2020.pdf b/04 Match/deep learning for matching in search and recommendation book 2020.pdf
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diff --git a/05 ReduceLatency/README.md b/05 ReduceLatency/README.md
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+- [[D2Q][KDD 22][Kuai Shou]Counterfactual Video Recommendation for Duration Debiasing](https://arxiv.org/pdf/2206.06003.pdf)
+- [[VR-NDT][WWW 23][Tencent]Reweighting Clicks with Dwell Time in Recommendation](https://arxiv.org/abs/2209.09000)
+- [[SQuAD][KDD 23][Microsoft]Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference](https://arxiv.org/abs/2306.14393)
+- [[LibAUC][KDD 23][Google] LibAUC: A Deep Learning Library for X-Risk Optimization](https://arxiv.org/abs/2306.03065)
+- [[KDD 23][Google]Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation](https://arxiv.org/abs/2306.01720)
+- [[KDD 23][Hua Wei]Deep Landscape Forecasting in Multi-Slot Real-Time Bidding](https://www.youtube.com/watch?v=TsAH4947gjM)
+- [[TPM][KDD 23][Kuai Shou]Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](https://arxiv.org/pdf/2306.03392.pdf)
+- [[KDD 23][ByteDance]Counterfactual Video Recommendation for Duration Debiasing]
+- [[RecAlg][CIKM 23][Webo]MemoNet: Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction](https://arxiv.org/pdf/2211.01334.pdf) [Note](https://mp.weixin.qq.com/s/7PGf7SLNJ9oHSTYJV72MYg)
+- [[KDD 23][Mei Tuan]A Multi-stage Framework for Online Bonus Allocation Based on Constrained User Intent Detection](https://www.youtube.com/watch?v=go055TZ7cLA)
+- [[AAAI 23][ByteDance]CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU](https://arxiv.org/pdf/2204.06240.pdf)
diff --git a/05 ReduceLatency/[DEFUSE][WWW 22][Alibaba] Asymptotically Unbiased Estimation for Delayed Feedback.pdf b/05 ReduceLatency/[DEFUSE][WWW 22][Alibaba] Asymptotically Unbiased Estimation for Delayed Feedback.pdf
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diff --git a/05 ReduceLatency/[FPT][KDD 22][MeiTuan] Applying Deep Learning Based Probabilistic Forecasting to Food.pdf b/05 ReduceLatency/[FPT][KDD 22][MeiTuan] Applying Deep Learning Based Probabilistic Forecasting to Food.pdf
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diff --git a/05 ReduceLatency/[KDD 18][Airbnb] Real-time Personalization using Embeddings for Search.pdf b/05 ReduceLatency/[KDD 18][Airbnb] Real-time Personalization using Embeddings for Search.pdf
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diff --git a/06 Diversity/README.md b/06 Diversity/README.md
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+- [[SASS][CIKM 22][Alibaba]Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://arxiv.org/abs/2208.11457)
+- [[PLATE][SIGIR 23][HuaWei]PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations](https://dl.acm.org/doi/10.1145/3539618.3591750)
+- 其他资源
+  - [一文纵览最近的多样性推荐文章](https://mp.weixin.qq.com/s/QvH2fys6o3BzvHWyucX30g)  
diff --git a/Diversity/[AAAI 20][Google] Pairwise Fairness for Ranking and Regression.pdf b/06 Diversity/[AAAI 20][Google] Pairwise Fairness for Ranking and Regression.pdf
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diff --git a/Diversity/[NeurIPS 18][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity.pdf b/06 Diversity/[NeurIPS 18][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity.pdf
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diff --git a/07 MTL/Extend/Dynamic Weight/[LHUC][TSALP 16] Learning Hidden Unit Contributions for.pdf b/07 MTL/Extend/Dynamic Weight/[LHUC][TSALP 16] Learning Hidden Unit Contributions for.pdf
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diff --git a/MTL/Extend/[ESM2][arxiv][Alibaba] Conversion Rate Prediction via Post-Click Behaviour Modeling.pdf b/07 MTL/Extend/[ESM2][arxiv][Alibaba] Conversion Rate Prediction via Post-Click Behaviour Modeling.pdf
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diff --git a/MTL/Extend/Perceive Your Users in Depth- Learning Universal User Representations from Multiple E-commerce Tasks.pdf b/07 MTL/Extend/[KDD 18][Alibaba] Perceive Your Users in Depth- Learning Universal User Representations from Multiple E-commerce Tasks.pdf
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diff --git a/MTL/Extend/[KDD 20][Alibaba] M2GRL_A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems.pdf b/07 MTL/Extend/[KDD 20][Alibaba] M2GRL_A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems.pdf
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diff --git a/MTL/Extend/[KDD 20][Alibaba] Multi-objective Optimization for Guaranteed Delivery in Video Service Platform.pdf b/07 MTL/Extend/[KDD 20][Alibaba] Multi-objective Optimization for Guaranteed Delivery in Video Service Platform.pdf
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diff --git a/MTL/Extend/[RecSys 19][Google] Recommending What Video to Watch Next_A Multitask Ranking System.pdf b/07 MTL/Extend/[RecSys 19][Google] Recommending What Video to Watch Next_A Multitask Ranking System.pdf
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diff --git a/07 MTL/Note/ESMM.md b/07 MTL/Note/ESMM.md
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+# ESMM
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/07 MTL/Note/MMOE.md b/07 MTL/Note/MMOE.md
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+# MMOE
+
+### 1.模型解读
+
+ ### 2.表达式
+
+### 3.一般步骤
+
+### 4.模型的优缺点
+
+### 5.改进版本
+
+### 6.Python实现
+
+### 7.参考资料
+
diff --git a/07 MTL/README.md b/07 MTL/README.md
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+# MTL
+- [[M2M][WSDM 22][Alibaba]Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](https://arxiv.org/abs/2201.06814)
+- [[COAST][WWW 23][Lenovo]Cross-domain recommendation via user interest alignment](https://arxiv.org/abs/2301.11467)
+- [[AdaTT][KDD 23][Meta]AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations](https://arxiv.org/pdf/2304.04959.pdf)
+- [[PEPNet][Arxiv 23][Kuaishou]PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/pdf/2302.01115.pdf)
+- [[STAN][Recsys 23][Shopee]STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://arxiv.org/abs/2306.12232)
+- [[ColdGPT][Recsys 23][salesforce]Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://arxiv.org/pdf/2306.14462.pdf)
+- [[KDD 23][Airbnb]Optimizing Airbnb Search Journey with Multi-task Learning](https://arxiv.org/abs/2305.18431)
+- [[KDD 23][Google] Improving Training Stability for Multitask Ranking Models in Recommender Systems](https://arxiv.org/abs/2302.09178)
+- [[KDD 23][Google]Boosting Multitask Learning on Graphs through Higher-Order Task Affinities](https://arxiv.org/abs/2306.14009)
+- [[COMET][KDD 23][Google] COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search](https://arxiv.org/abs/2306.02824)
+- [[ADL][SIGIR 23][CaiNiao]ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3539618.3591944)
+- [[SAMD][KDD 23][Ant]SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation](https://dl.acm.org/doi/pdf/10.1145/3580305.3599955)
+- [[MultiSFS][SIGIR 23][Hua Wei]Single-shot Feature Selection for Multi-task Recommendations](https://dl.acm.org/doi/abs/10.1145/3539618.3591767)
+- [[STEM][AAAI 24][Tencent]STEM: Unleashing the Power of Embeddings for Multi-task Recommendation](https://arxiv.org/pdf/2308.13537.pdf) [[Note]](https://mp.weixin.qq.com/s/sCF9bXUGokzwVg7jpZcu9w)
+- [[MOPPR][KDD 22][Alibaba]Multi-Objective Personalized Product Retrieval in Taobao Search](https://arxiv.org/ftp/arxiv/papers/2210/2210.04170.pdf) [Note](https://mp.weixin.qq.com/s/35LR52W4DwzaPrCfkK-fPw)
+
+# some resources
+
+- [Multi-task and Multi-scenario](https://mp.weixin.qq.com/s/4FRc-keU_4H8ZCYiKftqaA)
+- [Multi-task and Multi-scenario in MeiTuan](https://mp.weixin.qq.com/s/vsIStYa9wi4-bqquonHBww)
diff --git a/MTL/[AAAI 19][Google] SNR_Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning.pdf b/07 MTL/[AAAI 19][Google] SNR_Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning.pdf
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diff --git a/MTL/[AAAI 20] Learning Sparse Sharing Architectures for Multiple Tasks.pdf b/07 MTL/[AAAI 20] Learning Sparse Sharing Architectures for Multiple Tasks.pdf
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diff --git a/MTL/[ESMM][SIGIR 18][Alibaba] Entire Space Multi-Task Model_An Effective Approach for Estimating Post-Click Conversion Rate.pdf b/07 MTL/[ESMM][SIGIR 18][Alibaba] Entire Space Multi-Task Model_An Effective Approach for Estimating Post-Click Conversion Rate.pdf
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diff --git a/MTL/[KDD 20][Google] Multitask Mixture of Sequential Experts for User Activity Streams.pdf b/07 MTL/[KDD 20][Google] Multitask Mixture of Sequential Experts for User Activity Streams.pdf
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diff --git a/MTL/[MMoE][KDD 18][Google] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.pdf b/07 MTL/[MMoE][KDD 18][Google] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.pdf
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diff --git a/07 MTL/[Metabalance][WWW 22][MetaAI] MetaBalance Improving Multi-Task Recommendations via.pdf b/07 MTL/[Metabalance][WWW 22][MetaAI] MetaBalance Improving Multi-Task Recommendations via.pdf
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diff --git a/MTL/[RecSys 19][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation.pdf b/07 MTL/[RecSys 19][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation.pdf
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diff --git a/Sequential/Extend/[CMI][KDD 20][Alibaba] Controllable Multi-Interest Framework for Recommendation.pdf b/08 Sequential/Extend/[CMI][KDD 20][Alibaba] Controllable Multi-Interest Framework for Recommendation.pdf
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diff --git a/Sequential/Extend/[PeterRec][SIGIR 20][Tencent] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.pdf b/08 Sequential/Extend/[PeterRec][SIGIR 20][Tencent] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.pdf
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diff --git a/Sequential/Extend/[SIGIR 19][Alibaba] Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction.pdf b/08 Sequential/Extend/[SIGIR 19][Alibaba] Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction.pdf
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diff --git a/Sequential/Extend/[SIGIR 20][Tencent] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.pdf b/08 Sequential/Extend/[SIGIR 20][Tencent] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.pdf
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diff --git a/08 Sequential/README.md b/08 Sequential/README.md
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+- [[SURGE][SIGIR 21][Kuai shou]Sequential Recommendation with Graph Neural Networks](https://arxiv.org/abs/2106.14226)
+- [[adv][Arxiv 23][Salesforce]Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training](https://arxiv.org/abs/2304.05492)
+- [[DCRec][WWW 23][Tencent]Debiased Contrastive Learning for Sequential Recommendation](https://arxiv.org/abs/2303.11780)
+- [[MSM4SR][Arxiv 23][Alibaba]ultimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning](https://arxiv.org/abs/2303.11879)
+- [[ReSeq][Recsys 23][Boss Zhipin]Reciprocal Sequential Recommendation](https://arxiv.org/abs/2306.14712)
+- [[CT4Rec][KDD 23][Tencent/OPPO]CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation](https://www.youtube.com/watch?v=pX3aQRqDgy8)
+- [[KDD 23][Amazon]Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://arxiv.org/abs/2305.13731)
+- [[Arxiv 22][Salesforce]Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer](https://arxiv.org/abs/2210.13572)
+- [[Miracle][SIGIR 23][Ant]Towards Multi-Interest Pre-training with Sparse Capsule Network](https://dl.acm.org/doi/10.1145/3539618.3591778) [Note](https://mp.weixin.qq.com/s/EDpTxTj84XwPXTNsXCi24A)
+- [[STDP][Recsys 23][Mei Tuan]Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model](https://dl.acm.org/doi/pdf/10.1145/3604915.3608821)
+- [[ITSRec][Recsys 23][Amazon]Incorporating Time in Sequential Recommendation Models](https://dl.acm.org/doi/pdf/10.1145/3604915.3608830)
+- [[BERT4Rec vs SASRec][Recsys 23][Sberbank]Turning Dross Into Gold Loss: is BERT4Rec really better than
+SASRec?](https://dl.acm.org/doi/pdf/10.1145/3604915.3610644)
+- [[RUEL][CIKM 23][Microsoft]RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://arxiv.org/pdf/2309.10469.pdf)
+- [[Negative Feedback][Recsys 23][Google]Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://arxiv.org/abs/2308.12256) [note](https://mp.weixin.qq.com/s/5ey25PbhOFROSN0fhGcxXQ)
+  
+
+- Time Series
+  - [[KDD 23][Microsoft] Workplace Recommendation with Temporal Network Objectives](https://www.youtube.com/watch?v=5OWlKb81NlE)
diff --git a/08 Sequential/[Attention2D][WSDM 22][eBay] Sequential Modeling with Multiple Attributes for Watchlist.pdf b/08 Sequential/[Attention2D][WSDM 22][eBay] Sequential Modeling with Multiple Attributes for Watchlist.pdf
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diff --git a/Sequential/[GRU][CIKM 18] Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.pdf b/08 Sequential/[GRU][CIKM 18] Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.pdf
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diff --git a/Sequential/[IJCAI 19] Sequential Recommender Systems_Challenges, Progress and Prospects.pdf b/08 Sequential/[IJCAI 19] Sequential Recommender Systems_Challenges, Progress and Prospects.pdf
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diff --git a/Sequential/[Transformer][ICDM 18] Self-Attentive Sequential Recommendation.pdf b/08 Sequential/[Transformer][ICDM 18] Self-Attentive Sequential Recommendation.pdf
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diff --git a/09 UserModel/README.md b/09 UserModel/README.md
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+- [[PTUPCDR][WSDM 22][Tencent]Personalized Transfer of User Preferences for Cross-domain Recommendation](https://arxiv.org/abs/2110.11154)
+- [[LURM][KDD 23][ALibaba]Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling](https://arxiv.org/pdf/2110.11337.pdf)
+- [[TransAct][KDD 23][Pinterest]TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://arxiv.org/abs/2306.00248)
+- [[ReLoop2][KDD 23][Hua Wei]Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop](https://arxiv.org/abs/2306.08808)
+- [[PEPNET][KDD 23][Kuai Shou] PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115)
+- [[DPVP][KDD 23][Mei Tuan]Modeling Dual Period-Varying Preferences for Takeaway Recommendation](https://arxiv.org/abs/2306.04370)
+- [[KDD 23][Mei Tuan]NEON: Living Needs Prediction System in Meituan]
+- [[CMIL][Recsys 23][Wechat]Interpretable User Retention Modeling in Recommendation](https://dl.acm.org/doi/abs/10.1145/3604915.3608818) [解读](https://mp.weixin.qq.com/s/h5jOXOlQOMI_Aygs0teWKw)
+- Multi Behavior
+  - [[KDD 23][Hua Wei]Hierarchical Projection Enhanced Multi-behavior Recommendation](https://www.youtube.com/watch?v=VLlUSAGL7zg)
diff --git a/UserModel/[KDD 19][Tencent] A User-Centered Concept Mining System for Query and Document Understanding at Tencent.pdf b/09 UserModel/[KDD 19][Tencent] A User-Centered Concept Mining System for Query and Document Understanding at Tencent.pdf
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diff --git a/KG/Extend/[AAAI 19][Ebay] Explainable Reasoning over Knowledge Graphs for Recommendation.pdf b/10 KG/Extend/[AAAI 19][Ebay] Explainable Reasoning over Knowledge Graphs for Recommendation.pdf
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diff --git a/KG/Extend/[ATBRG][SIGIR 20][Alibaba] ATBRG_Adaptive Target-Behavior Relational Graph Network for Effective Recommendation.pdf b/10 KG/Extend/[ATBRG][SIGIR 20][Alibaba] ATBRG_Adaptive Target-Behavior Relational Graph Network for Effective Recommendation.pdf
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diff --git a/KG/Extend/[DLP-KDD 19] An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation.pdf b/10 KG/Extend/[DLP-KDD 19] An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation.pdf
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diff --git a/KG/Extend/[KDD 19][Meituan] Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.pdf b/10 KG/Extend/[KDD 19][Meituan] Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.pdf
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diff --git a/KG/Extend/[WWW 19][Microsoft] Knowledge Graph Convolutional Networks for Recommender Systems.pdf b/10 KG/Extend/[WWW 19][Microsoft] Knowledge Graph Convolutional Networks for Recommender Systems.pdf
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diff --git a/KG/Extend/[WWW 19][Microsoft] Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation.pdf b/10 KG/Extend/[WWW 19][Microsoft] Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation.pdf
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diff --git a/10 KG/README.md b/10 KG/README.md
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+
diff --git a/KG/[KGAT][KDD 19] KGAT_Knowledge Graph Attention Network for Recommendation.pdf b/10 KG/[KGAT][KDD 19] KGAT_Knowledge Graph Attention Network for Recommendation.pdf
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diff --git a/KG/[RippleNet][CIKM 18] RippleNet_Propagating User Preferences on the Knowledge Graph for Recommender Systems.pdf b/10 KG/[RippleNet][CIKM 18] RippleNet_Propagating User Preferences on the Knowledge Graph for Recommender Systems.pdf
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diff --git a/11 BERT/README.md b/11 BERT/README.md
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+
diff --git a/BERT/Transformer/Adaptive Attention Span in Transformers.pdf b/11 BERT/Transformer/Adaptive Attention Span in Transformers.pdf
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diff --git a/BERT/Transformer/Augmenting Self-attention with Persistent Memory.pdf b/11 BERT/Transformer/Augmenting Self-attention with Persistent Memory.pdf
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diff --git a/BERT/Transformer/BP-Transformer_Modelling Long-Range Context via Binary Partitioning.pdf b/11 BERT/Transformer/BP-Transformer_Modelling Long-Range Context via Binary Partitioning.pdf
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diff --git a/BERT/Transformer/Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling.pdf b/11 BERT/Transformer/Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling.pdf
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diff --git a/BERT/Transformer/DiSAN_Directional Self-Attention Network for RNN_CNN-Free Language Understanding.pdf b/11 BERT/Transformer/DiSAN_Directional Self-Attention Network for RNN_CNN-Free Language Understanding.pdf
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diff --git a/BERT/Transformer/Generating Long Sequences with Sparse Transformers.pdf b/11 BERT/Transformer/Generating Long Sequences with Sparse Transformers.pdf
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diff --git a/BERT/[ALBERT][arxiv 19][Google] ALBERT_A Lite BERT for Self-supervised Learning of Language Representations.pdf b/11 BERT/[ALBERT][arxiv 19][Google] ALBERT_A Lite BERT for Self-supervised Learning of Language Representations.pdf
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diff --git a/BERT/[BERT][arxiv 19][Google ]BERT_Pre-training of Deep Bidirectional Transformers for Language Understanding.pdf b/11 BERT/[BERT][arxiv 19][Google ]BERT_Pre-training of Deep Bidirectional Transformers for Language Understanding.pdf
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diff --git a/BERT/[ERNIE][arxiv 19][Baidu] ERNIE_Enhanced Representation through Knowledge Integration.pdf b/11 BERT/[ERNIE][arxiv 19][Baidu] ERNIE_Enhanced Representation through Knowledge Integration.pdf
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diff --git a/BERT/[T5][arxiv 19][Google] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.pdf b/11 BERT/[T5][arxiv 19][Google] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.pdf
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diff --git a/BERT/[XLNet][arxiv 19][Google] XLNet_Generalized Autoregressive Pretraining for Language Understanding.pdf b/11 BERT/[XLNet][arxiv 19][Google] XLNet_Generalized Autoregressive Pretraining for Language Understanding.pdf
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diff --git a/12 Debias/README.md b/12 Debias/README.md
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+- [[DB-VAE][Arxiv 23]Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation](https://arxiv.org/abs/2306.15961)
+- [[TASTE][CIKM 23]](arxiv.org/abs/2308.14029) [note](https://mp.weixin.qq.com/s/vKFHXiGuIAZKq3GHOsuSmw)
+- video length
+  - [[VLDRec][TOIS 23]Alleviating Video-Length Effect for Micro-video Recommendation](https://arxiv.org/pdf/2308.14276.pdf)
+- some resources
+  - [position-bias建模](https://mp.weixin.qq.com/s/ERMWm4wWCjWBvTJJcT1fbw)
+  - [Debias](https://mp.weixin.qq.com/s/ATZCKdUQe25mpgNwxqwHYg)
diff --git a/Addressing Marketing Bias in Product Recommendations.pdf b/12 Debias/[WSDM2020] Addressing Marketing Bias in Product Recommendations.pdf
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diff --git a/13 EE/README.md b/13 EE/README.md
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+  - [[(LRP-Bandit][KDD 23][Alibaba]Efficient Sparse Linear Bandits under High Dimensional Data](https://www.mcwei.com/Research/Lasso_RP_Bandit.pdf)
diff --git a/EE/[LinUCB][WWW 10][Yahoo] A Contextual-Bandit Approach to Personalized News Article Recommendation.pdf b/13 EE/[LinUCB][WWW 10][Yahoo] A Contextual-Bandit Approach to Personalized News Article Recommendation.pdf
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diff --git a/Graph/Embedding/Extend/[WSDM 20][Tencent] Initialization for Network Embedding_A Graph Partition.pdf b/14 Graph/Embedding/Extend/[WSDM 20][Tencent] Initialization for Network Embedding_A Graph Partition.pdf
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diff --git a/Graph/Embedding/[DeepWak][KDD 14] DeepWalk_Online Learning of Social Representations.pdf b/14 Graph/Embedding/[DeepWak][KDD 14] DeepWalk_Online Learning of Social Representations.pdf
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diff --git a/Graph/Embedding/[GraRep][CIKM 15] GraRep_Learning Graph Representations with Global Structural Information.pdf b/14 Graph/Embedding/[GraRep][CIKM 15] GraRep_Learning Graph Representations with Global Structural Information.pdf
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diff --git a/Graph/Embedding/[HOPE][KDD 16] Asymmetric Transitivity Preserving Graph Embedding.pdf b/14 Graph/Embedding/[HOPE][KDD 16] Asymmetric Transitivity Preserving Graph Embedding.pdf
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diff --git a/Graph/Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf b/14 Graph/Embedding/[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf
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diff --git a/Graph/Embedding/[NetMF][WSDM 18] Network Embedding as Matrix Factorization_Unifying DeepWalk, LINE, PTE, and node2vec.pdf b/14 Graph/Embedding/[NetMF][WSDM 18] Network Embedding as Matrix Factorization_Unifying DeepWalk, LINE, PTE, and node2vec.pdf
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diff --git a/Graph/Embedding/[NetSMF][WWW 19] NetSMF_Large-Scale Network Embedding as Sparse Matrix.pdf b/14 Graph/Embedding/[NetSMF][WWW 19] NetSMF_Large-Scale Network Embedding as Sparse Matrix.pdf
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diff --git a/Graph/Embedding/[Node2Vec][KDD 16] Node2Vec_Scalable Feature Learning for Networks.pdf b/14 Graph/Embedding/[Node2Vec][KDD 16] Node2Vec_Scalable Feature Learning for Networks.pdf
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diff --git a/14 Graph/HyperGraph/[AutoIntent][KDD 22][MeiTuan] Automatically Discovering User Consumption Intents in Meituan.pdf b/14 Graph/HyperGraph/[AutoIntent][KDD 22][MeiTuan] Automatically Discovering User Consumption Intents in Meituan.pdf
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diff --git a/Graph/NN/Extend/[KDD 20][Alibaba] Understanding Negative Sampling in Graph Representation Learning.pdf b/14 Graph/NN/Extend/[KDD 20][Alibaba] Understanding Negative Sampling in Graph Representation Learning.pdf
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diff --git a/14 Graph/NN/[FederatedScope][KDD 22 best paper][Alibaba] FederatedScope-GNN Towards a Unified, Comprehensive and.pdf b/14 Graph/NN/[FederatedScope][KDD 22 best paper][Alibaba] FederatedScope-GNN Towards a Unified, Comprehensive and.pdf
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diff --git a/Graph/NN/[PinSage][KDD 18][Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf b/14 Graph/NN/[PinSage][KDD 18][Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf
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diff --git a/14 Graph/README.md b/14 Graph/README.md
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+- [[BM3][WWW 23][Alibaba]Bootstrap Latent Representations for Multi-modal Recommendation](https://arxiv.org/abs/2207.05969)
+- [[AutoCF][WWW 23][Tencent]Automated Self-Supervised Learning for Recommendation](https://arxiv.org/abs/2303.07797)
+- [[GALM][KDD 23][Amazon]Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications](https://arxiv.org/abs/2306.02592)
+- [[GetPt][KDD 23][Microsoft]GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network]
+- [[JGCF][KDD 23][Microsoft]On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://arxiv.org/abs/2306.03624)
+- [[HUGE][KDD 23][Google]HUGE: Huge Unsupervised Graph Embeddings with TPUs](https://www.youtube.com/watch?v=SWEpvDbpbcQ)
+- [[KDD 23][ByteDance]Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems](https://arxiv.org/abs/2305.16391)
diff --git a/15 LongTail/README.md b/15 LongTail/README.md
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+- [[MELT][SIGIR 23]MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation](https://arxiv.org/abs/2304.08382)
+- [[MGL][KDD 23][Alibaba]Meta Graph Learning for Long-tail Recommendation](https://www.youtube.com/watch?v=354oHUuI_O0)
+- [[CDN][KDD 23][Google] Empowering Long-tail Item Recommendation through Cross Decoupling Network](https://arxiv.org/abs/2210.14309)
+
diff --git a/15 LongTail/[MIRec][WWW 21][Google] A Model of Two Tales Dual Transfer Learning Framework for.pdf b/15 LongTail/[MIRec][WWW 21][Google] A Model of Two Tales Dual Transfer Learning Framework for.pdf
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diff --git a/16 NLP4Rec/README.md b/16 NLP4Rec/README.md
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+
+### LLM for Rec
+- Survey
+  - [Recommender Systems in the Era of
+Large Language Models (LLMs)](https://arxiv.org/pdf/2307.02046.pdf)
+  - [[LLM4Rec][Recsys 23]](https://arxiv.org/abs/2309.01157) [Note](https://mp.weixin.qq.com/s/PDKvIqrKQnMKK6WFqK5TXg)
+- [[P5][Arxiv 22]Recommendation as Language Processing (RLP):A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://arxiv.org/abs/2203.13366)
+- [ChatRec-arxiv23-Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://arxiv.org/abs/2303.14524)
+- [GPT4Rec-arxiv23-Amazon-GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation](https://arxiv.org/pdf/2304.03879.pdf)
+- [NIR-arxiv23-Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/pdf/2304.03153.pdf)
+- [PEPLER-arxiv23-Personalized Prompt Learning for Explainable Recommendation](https://arxiv.org/pdf/2202.07371.pdf)
+- [PLMRec-arxiv23-Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems](https://arxiv.org/pdf/2302.03735.pdf)
+- [[InstructRec][Arxiv 23][Tencent]Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://arxiv.org/abs/2305.07001)
+- [[LLM2ZeroShot][Arxiv 23][Tencent]Large Language Models are Zero-Shot Rankers for Recommender Systems](https://arxiv.org/abs/2305.08845)
+- [[ChatGPT][Arxiv 23][Alibaba]Is ChatGPT a Good Recommender? A Preliminary Study](https://arxiv.org/pdf/2304.10149.pdf)
+- [[FaiRLLM][Recsys 23]Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://arxiv.org/pdf/2305.07609.pdf)
+- [[MINT][Recsys 23]Large Language Model Augmented Narrative Driven Recommendations](https://arxiv.org/abs/2306.02250)
+- [[GPTRec][SIGIR 23]Generative Sequential Recommendation with GPTRec](https://arxiv.org/abs/2306.11114)
+- [[ANN][Arxiv 23][Google]Recommender Systems with Generative Retrieval](https://arxiv.org/abs/2305.05065)
+- [[QUERT][KDD 23][Alibaba]QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search](https://arxiv.org/abs/2306.06707)
+- [[LightToken][KDD 23][Amazon]LightToken: A Task and Model-agnostic Lightweight Token Embedding Framework for Pre-trained Language Models](https://www.youtube.com/watch?v=h-1kncLaK9s)
+- [[IVAE][KDD 23][Amazon]Exploiting Intent Evolution in E-commercial Query Recommendation](https://www.amazon.science/publications/exploiting-intent-evolution-in-e-commercial-query-recommendation)
+- [[RecruitPro][KDD 23][Bai Du]RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment](https://www.youtube.com/watch?v=njnk9C5CeDw)
+- [[PLATE][SIGIR 23][Hua Wei]PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations](https://dl.acm.org/doi/10.1145/3539618.3591750)
+- [[LLMSeq][Recsys 23]Leveraging Large Language Models for Sequential Recommendation](https://dl.acm.org/doi/pdf/10.1145/3604915.3610639)
+- [[LCRec][Arxiv2023][Tencent]Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://arxiv.org/abs/2311.09049) [[Code]](https://github.com/RUCAIBox/LC-Rec) [[Note]](https://mp.weixin.qq.com/s/ql9Ig_8LETCIyGSVifMbpw)
+- [[LLM+CRS][Arxiv2023]Leveraging Large Language Models in Conversational Recommender Systems](https://arxiv.org/abs/2305.07961) [[Note]](https://mp.weixin.qq.com/s/CYW2_R9dLNHiZcSGokFfZw)
+
+
+### Diffusion for Rec
+- [[DiffuRec][Arxiv 23]DiffuRec: A Diffusion Model for Sequential Recommendation](https://arxiv.org/abs/2304.00686)
+
+### Other Resources
+- [LLM for Rec](https://mp.weixin.qq.com/s/7yGqQBN8Lz_OhRW_1SxIcw)
+- [ID-based & LLM-based](https://mp.weixin.qq.com/s/L5qNHLJpBUpwt1NofApBew)
diff --git a/16 NLP4Rec/[NLP4Rec][Recsys 22] Recommendation as Language Processing (RLP).pdf b/16 NLP4Rec/[NLP4Rec][Recsys 22] Recommendation as Language Processing (RLP).pdf
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diff --git a/RL/Extend/[AAAI 19][Huawei] Large-scale Interactive Recommendation with Tree-structured Policy Gradient.pdf b/17 RL/Extend/[AAAI 19][Huawei] Large-scale Interactive Recommendation with Tree-structured Policy Gradient.pdf
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diff --git a/RL/Extend/[AAAI 20][ByteDance] Deep Reinforcement Learning for Online Advertising in Recommender Systems.pdf b/17 RL/Extend/[AAAI 20][ByteDance] Deep Reinforcement Learning for Online Advertising in Recommender Systems.pdf
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diff --git a/RL/Extend/[AAAI19][Alibaba] Virtual Taobao_Virtualizing Real-world Online Retail Environment for Reinforcement Learning.pdf b/17 RL/Extend/[AAAI19][Alibaba] Virtual Taobao_Virtualizing Real-world Online Retail Environment for Reinforcement Learning.pdf
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diff --git a/RL/Extend/[CIKM 18][Alibaba] Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising.pdf b/17 RL/Extend/[CIKM 18][Alibaba] Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising.pdf
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diff --git a/RL/Extend/[HATCH][WWW 20][Didi]Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation.pdf b/17 RL/Extend/[HATCH][WWW 20][Didi]Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation.pdf
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diff --git a/RL/Extend/[ICML 19][Alibaba] Generative Adversarial User Model for Reinforcement Learning Based Recommendation System.pdf b/17 RL/Extend/[ICML 19][Alibaba] Generative Adversarial User Model for Reinforcement Learning Based Recommendation System.pdf
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diff --git a/RL/Extend/[KDD 19][JD] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems.pdf b/17 RL/Extend/[KDD 19][JD] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems.pdf
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diff --git a/RL/Extend/[RecSys18][JD] Deep Reinforcement Learning for Page-wise Recommendations.pdf b/17 RL/Extend/[RecSys18][JD] Deep Reinforcement Learning for Page-wise Recommendations.pdf
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diff --git a/RL/Extend/[arxiv][ByteDance] Jointly Learning to Recommend and Advertise.pdf b/17 RL/Extend/[arxiv][ByteDance] Jointly Learning to Recommend and Advertise.pdf
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diff --git a/17 RL/README.md b/17 RL/README.md
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+- [[DT4Rec][WWW 23][Bai Du]User Retention-oriented Recommendation with Decision Transformer](https://arxiv.org/pdf/2303.06347.pdf)
+- [[DAU][WWW 23][Kuai shou]Reinforcing User Retention in a Billion Scale Short Video Recommender System](https://arxiv.org/abs/2302.01724)
+- [[MiRO][KDD 23][Alibaba]Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://arxiv.org/abs/2306.07106)
+- [[BLTP][KDD 23][Alibaba]RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads](https://arxiv.org/abs/2302.02592)
+- [[VRS][KDD 23][Meta]Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning](https://arxiv.org/abs/2306.03293)
+- [[KDD 23][Amazon]Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring](https://www.amazon.science/publications/experimentation-platforms-meet-reinforcement-learning-bayesian-sequential-decision-making-for-continuous-monitoring)
diff --git a/17 RL/[DPIN][SIGIR 22][MeiTuan] Deep Page-Level Interest Network in Reinforcement Learning for.pdf b/17 RL/[DPIN][SIGIR 22][MeiTuan] Deep Page-Level Interest Network in Reinforcement Learning for.pdf
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diff --git a/RL/[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation.pdf b/17 RL/[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation.pdf
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diff --git a/RL/[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodology.pdf b/17 RL/[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodology.pdf
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diff --git a/RL/[WSDM 19][Google] Top-K Off-Policy Correction for a REINFORCE Recommender System.pdf b/17 RL/[WSDM 19][Google] Top-K Off-Policy Correction for a REINFORCE Recommender System.pdf
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diff --git a/RL/[WWW 20][Google] Off-policy Learning in Two-stage Recommender Systems.pdf b/17 RL/[WWW 20][Google] Off-policy Learning in Two-stage Recommender Systems.pdf
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diff --git a/18 Others/GBDT-wepon.pdf b/18 Others/GBDT-wepon.pdf
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+
+- [Awesome-Contrastive-Learning-and-Data-Augmentation-RS-Paper-Code](https://github.com/QinHsiu/Awesome-Contrastive-Learning-and-Data-Augmentation-RS-Paper-Code)
+- [[DNS][WWWW 23]On the Theories Behind Hard Negative Sampling for Recommendation](https://arxiv.org/abs/2302.03472)
+- [[FairNeg][WWW 23]Fairly Adaptive Negative Sampling for Recommendations](https://arxiv.org/abs/2302.08266)
+- Multi Modality
+  - [[MODEST][SIGIR 23]Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](https://arxiv.org/pdf/2306.14179.pdf)
+  - [[FedMultiModel][KDD 23][Amazon] FedMultimodal: A Benchmark for Multimodal Federated Learning](https://arxiv.org/pdf/2306.09486.pdf)
+  - [[MMSR][CIKM 23][Hua Wei]Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems](https://github.com/HoldenHu/MMSR) [Note](https://mp.weixin.qq.com/s/vtOVv8wt4SfzMXg-r0VVZQ)
+  - [[IDvs.MoRec][SIGIR 23]]( https://arxiv.org/abs/2303.1383) [解读](https://mp.weixin.qq.com/s/16xYxHyUky0Dc2pgtyNWFA)
+    
+
+- Generate
+  - [[GeneRec][Arxiv 23]Generative Recommendation: Towards Next-generation Recommender Paradigm](https://arxiv.org/abs/2304.03516)
+  - [[FNAS][WWW 23][HUa Wei]FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation](https://arxiv.org/abs/2304.00545)
+- [[FPSR][WWW 23]Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://arxiv.org/abs/2207.05959)
+- [[APR][SIGIR 23][Wei Xin]Attacking Pre-trained Recommendation](https://arxiv.org/pdf/2305.03995.pdf)
+- [[DPVP][KDD 23][Mei Tuan]Modeling Dual Period-Varying Preferences for Takeaway Recommendation](https://arxiv.org/abs/2306.04370)
+
+- Cross-Doman
+  - [[CCTL][KDD 23][Mei Tuan]A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://arxiv.org/pdf/2306.16425.pdf)
+  - [[CDCF][Arxiv 23]Cross-domain Recommender Systems via Multimodal Domain Adaptation](https://arxiv.org/pdf/2306.13887.pdf)
+  - [[DRM][KDD 23][Microsoft]Domain-Specific Risk Minimization for Domain Generalization](https://openreview.net/forum?id=vCVTZYFcmCm)
+  - [[SMILE][KDD 23][Google]SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding](https://paperswithcode.com/paper/smile-evaluation-and-domain-adaptation-for)
+  - [[CCTL][KDD 23][Mei Tuan]A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://arxiv.org/abs/2306.16425)
+  - [[HAMUR][CIKM 23][Hua Wei]HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://arxiv.org/pdf/2309.06217.pdf) [note](https://mp.weixin.qq.com/s/lmxNVOElZ9MnuarZzEiD_g)
+  - [[SATrans][KDD 23]Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3580305.3599936) [note](https://mp.weixin.qq.com/s/igY0nXOdM7sclC3M9fL1oA)
+
+- Social
+  - [[CSCR][KDD 23][Tencent]Constrained Social Community Recommendation](https://www.youtube.com/watch?v=b14qgetaXxs)
+- Negative Feedback
+  - [[FeedBack][CIKM 23][Kuai shou]Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System](https://arxiv.org/pdf/2308.13249.pdf) 
+
+- Federated Learning
+  - [[KDD 23][Microsoft]FedDefender: Client-Side Attack-Tolerant Federated Learning]
+  - [[UA-FedRec][KDD 23][Microsoft]UA-FedRec: Untargeted Attack on Federated News Recommendation](https://arxiv.org/abs/2202.06701)
+  - 
+- Consistency Modeling
+  - [SIGIR 18-20](https://mp.weixin.qq.com/s/ERMWm4wWCjWBvTJJcT1fbw)
+ 
+- Online
+  - [[KDD 23][Airbnb]Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes](https://alexdeng.github.io/public/files/kdd2023-inexp.pdf)
+    
+- Tag
+  - [[KDD 23][Tencent]Doctor Specific Tag Recommendation for Online Medical Record Management](https://www.youtube.com/watch?v=TopyznCvTuQ)
+  - [[KDD 23][Microsoft]Deep Encoders with Auxiliary Parameters for Extreme Classification](https://www.youtube.com/watch?v=4vGX2H780KY)
+  - [[CLUR][KDD 23][Microsoft]CLUR: Uncertainty Estimation for Few-Shot Text Classification with Contrastive Learning](https://www.youtube.com/watch?v=SvJCWdSkNTQ)
+
+- Search
+  - [[(LRP-Bandit][KDD 23][Alibaba]Efficient Sparse Linear Bandits under High Dimensional Data](https://www.mcwei.com/Research/Lasso_RP_Bandit.pdf)
+  - [[CCG][KDD 23][Alibaba]E-commerce Search via Content Collaborative Graph Neural Network](https://www.youtube.com/watch?v=AttOef4lecg)
+  - [[TW-BERT][KDD 23][Google]End-to-End Query Term Weighting](https://research.google/pubs/pub52462/)
+  - [[S2Phere][KDD 23][Bai Du]S2phere: Semi-Supervised Pre-training for Web Search over Heterogeneous Learning to Rank Data](https://www.youtube.com/watch?v=_Khb0rKdaL0)
+  - [[KDD 23][Bai Du]Learning Discrete Document Representations in Web Search](https://www.youtube.com/watch?v=KV8ERmtZtkw)
+  - [[SE-DSI][KDD 23][Bai Du]Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies](https://arxiv.org/abs/2305.15115)
+  - [[PGLBox][KDD 23][Bai Du]PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation](https://www.youtube.com/watch?v=LClNDNnNA7g)
+  - [[Markplace][WWW 23][FaceBook]](https://mp.weixin.qq.com/s/CcRAKDZJMqq6jTfqGM6g1A)
+
+- AD
+  - [[KDD 23][Alibaba]End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery Advertising](https://www.youtube.com/watch?v=Ghh8qRZDFJw)
+  - [[KDD 23][Alibaba]Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach](https://www.google.com.hk/search?q=Learning-Based+Ad+Auction+Design+with+Externalities%3A+The+Framework+and+A+Matching-Based+Approach&oq=Learning-Based+Ad+Auction+Design+with+Externalities%3A+The+Framework+and+A+Matching-Based+Approach&aqs=chrome..69i57.275j0j4&sourceid=chrome&ie=UTF-8)
+  - [[KDD 23][Alibaba]A Personalized Automated Bidding Framework for Fairness-aware Online Advertising](https://www.youtube.com/watch?v=uOrqHngy2eY)
+  - [[PASS][KDD 23][Microsoft]PASS: Personalized Advertiser-aware Sponsored Search](https://www.youtube.com/watch?v=L7Qb23w-D7I)
+    
diff --git a/18 Others/Rec.md b/18 Others/Rec.md
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+# 推荐中常见的问题
+
+### 1.常常存在的问题
+
+- 大数据、稀疏、长尾、噪音
+- 用户行为模式的复杂性
+- 冷启动（新用户、新商品）
+- 多样性与精确性
+- 用户UI与用户体验（个性化体验的可解释性）
+
+- [短视频推荐视频时长bias问题 (qq.com)](https://mp.weixin.qq.com/s/uwu4sI6qqbRhI3Z6CbPSMQ)
+- [百度搜索召回调研 (qq.com)](https://mp.weixin.qq.com/s/W2FA4VRX8oG8dUn6z8IQ2Q)
+- [小红书冷启动技术&指标体系(一) (qq.com)](https://mp.weixin.qq.com/s/xCzf1fhnsDLcZvz1QETE7w)
+- [小红书冷启动技术&流量调控(二) (qq.com)](https://mp.weixin.qq.com/s/YLKSRQqZ0MIFWkBAViW4Fw)
+- [小红书冷启动技术&冷启召回(三) (qq.com)](https://mp.weixin.qq.com/s?__biz=MzU0MDA1MzI0Mw==&mid=2247493726&idx=1&sn=632644be022bfe72d45dd45cf61eeb4b&chksm=fb3db3cacc4a3adcf3f36e2a191630031754a7b30df403fe49b9d89261c8401cedf5130f2876&scene=178&cur_album_id=2709346850864332801#rd)
+- [小红书冷启动技术&AB方案(四) (qq.com)](https://mp.weixin.qq.com/s/mXW184hPUceTqzF0iA4_gA)
+- [京东零售在电商搜索场景下的数据科学实践 (qq.com)](https://mp.weixin.qq.com/s/Rmgh-ynoWU4uerh_4jL4YA)
+- [火山引擎DataTester：A/B测试，让企业摆脱广告投放“乱烧钱” (qq.com)](https://mp.weixin.qq.com/s/SFfMm2wipaQFxypCnSCoCQ)
+- [阿里基于渠道协同的预算分配与权益管理实践 (qq.com)](https://mp.weixin.qq.com/s/FalJ0t-5xukMPWWUufW3eA)
+- [小米电商推荐算法CVR模型实践 (qq.com)](https://mp.weixin.qq.com/s/dByUTt6PloT0FS3_jxjQag)
+- [TBB三剑客：万字入门推荐系统 (qq.com)](https://mp.weixin.qq.com/s/rbxtM4zBVpH6Bl1ITrW-AQ)
+- [入"坑"推荐系统，从Google这篇课程开始 (qq.com)](https://mp.weixin.qq.com/s/t5kMA30QJh1TPx7w4gJ2lg)
+- [【实践】推荐、搜索、广告多业务多场景统一预估引擎实践与思考 (qq.com)](https://mp.weixin.qq.com/s/dl8O98x5-psI10SFnK1TxA)
+- [涨点利器 | 双塔模型涨点分享 (二) (qq.com)](https://mp.weixin.qq.com/s/xo_p8J9VBx9zeWdF_qAErA)
+- [推荐系统中稀疏特征Embedding的优化表示方法 (qq.com)](https://mp.weixin.qq.com/s/IsC8bpdsWeLBgwevAfoAwA)
+- [如何做好内容：小红书爆文笔记进阶指南（附下载链接） (qq.com)](https://mp.weixin.qq.com/s/aWxucHLmbUqkvw_J5lFK0w)
+- [美团实践：交互式推荐在外卖场景的探索与应用 (qq.com)](https://mp.weixin.qq.com/s/WBLsv4NbCIoA7d-0sgZVKg)
+- [大佬总结: 广告算法和推荐算法的五大差异 (qq.com)](https://mp.weixin.qq.com/s/HnAZQ6N7rrPYav7OVGNE9w)
+- [基于决策树的特征选择 (qq.com)](https://mp.weixin.qq.com/s/wliq1gBBFgCUUShnlojB7w)
+- [再谈排序算法的pairwise，pointwise，listwise (qq.com)](https://mp.weixin.qq.com/s/PFrndBfO9aV6IiCCWSpHwg)
+- [如何构建好的用户画像平台？ (qq.com)](https://mp.weixin.qq.com/s/nqmUnVy4LcRoO_UsNBK5Sw)
+
+### 2.与新颖技术的结合
+
+- [罗清：对比学习在快手推荐排序的应用 (qq.com)](https://mp.weixin.qq.com/s/2GgIt38uO_qmLGskfB_lfQ)
+- [预训练模型在信息流推荐中的应用与探索 (qq.com)](https://mp.weixin.qq.com/s/bAT6Ai4gmN1LOAlTbTaP8A)
+- [自动化知识图谱表示学习：从三元组到子图 (qq.com)](https://mp.weixin.qq.com/s/rhhaUAurgjHpF822XNo6zw)
+- [他山之石 | 腾讯图神经网络与推荐预训练模型 (qq.com)](https://mp.weixin.qq.com/s/IM6lixoTnnXXt_QDfOBMlQ)
+- [达摩院多模态预训练模型的轻量适配技术分享 (qq.com)](https://mp.weixin.qq.com/s/3nYtRkM5OTGB3fAOMoM99g)
+- [华为在联邦广告算法上的探索及应用 (qq.com)](https://mp.weixin.qq.com/s/0w2Ud9-jBZUt2wO3GhtmaA)
+- [AAAI'23 两篇大厂CTR预估最新工作 (qq.com)](https://mp.weixin.qq.com/s/BXY6gQEcvTh6b1jpLHC9QA)
+
+### 3. 实践
+
+- [百度凤巢, 1 个精排模型搞定一切~ (qq.com)](https://mp.weixin.qq.com/s?__biz=MzIyNDY5NjEzNQ==&mid=2247494733&idx=3&sn=efcd725bbfe274926c0afa1455ad0068&chksm=e809ae65df7e2773aba37374ba27c8611930a3620bc15b0e948d19d063bccac0253e179622fd&mpshare=1&scene=24&srcid=03238rvMJ6cV1D20mpne5487&sharer_sharetime=1679549779061&sharer_shareid=fb2e0bd32addc3a46aa1ebeb1937de44#rd)
+- [【实践】美团外卖首页Feed在搭建交互式推荐时遇到的挑战和解决思路 (qq.com)](https://mp.weixin.qq.com/s/PoYwVce6S1aHarVGzIedTg)
+- [新闻推荐实战 (八) : 前后端交互 (qq.com)](https://mp.weixin.qq.com/s/peMnEMQTe08pvMIMrWFRBw)
+- [在C++平台上部署PyTorch模型流程+踩坑实录 (qq.com)](https://mp.weixin.qq.com/s/vu9vfMrPGhznSpGYIE-4eA)
+- [张俊林：推荐系统排序环节特征 Embedding 建模 (qq.com)](https://mp.weixin.qq.com/s/LgW75OmB-jNysuttyOVr6w)
+- [广告推荐CTR点击率预测实践项目！ (qq.com)](https://mp.weixin.qq.com/s/rpkulvviD4lPYWybkZkQzw)
+- [Docs (feishu.cn)](https://datawhaler.feishu.cn/docx/doxcnufyNTvUfpU57sRyydgyK6c)
+- [kaggle实战：用户个性化分析与分群 (qq.com)](https://mp.weixin.qq.com/s/9PkyrDn8tKl5PYBrB_0kPw)
+- [轻量级图卷积网络LightGCN详解与实践 (qq.com)](https://mp.weixin.qq.com/s/G2SEydgOI09FqtpMvWZKvw)
+- [DSSM双塔模型及其Pytorch实现 (qq.com)](https://mp.weixin.qq.com/s/wYlduk3lVKq_bSr1ujDZ_w)
+- [熬了一晚上，我从零实现了Transformer模型，把代码讲给你听 (qq.com)](https://mp.weixin.qq.com/s/4Be3x1EBz6uWrJ76bGjZNg)
+- [30 分钟看懂 XGBoost（Python代码） (qq.com)](https://mp.weixin.qq.com/s/eAVcbnsh9zzVnlOFz9w5Dw)
+- [可视化方式，彻底搞懂LSTM (qq.com)](https://mp.weixin.qq.com/s/xNej2HsvMJq8hRb6ujkSjw)
+- [手撕 CNN 经典网络之 VGGNet（PyTorch实战篇） (qq.com)](https://mp.weixin.qq.com/s/SzWluooh3pPOed4loEHRqA)
+- [动手实现DeepFM (qq.com)](https://mp.weixin.qq.com/s/_P9jblhde2OyNUaqcYgLuA)
+- [推荐系统相关资源介绍（书籍、代码、综述、教程等内容） (qq.com)](https://mp.weixin.qq.com/s/5hANTWuH8_WZJkAA1h45_w)
+- [Calibration4CVR：2018年关于“神经元级别共享的多任务CVR”的初探 (qq.com)](https://mp.weixin.qq.com/s/2JFY--9xDoMF5_2YCehwkg)
+
diff --git "a/KeyNote/[2019] DLRM_\347\272\252\345\216\232\344\270\232.pdf" "b/18 Others/[2019] DLRM_\347\272\252\345\216\232\344\270\232.pdf"
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+++ b/20 Cold Start/README.md	
@@ -0,0 +1 @@
+- [[USR][CIKM 23][Ant]An Unified Search and Recommendation Foundation Model for Cold-Start Scenario](https://arxiv.org/pdf/2309.08939.pdf) [note](https://mp.weixin.qq.com/s/Efj7yda_io24C6-RVv96xQ)
diff --git a/20 Cold Start/README.txt b/20 Cold Start/README.txt
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diff --git a/CTR/[DeepFM][IJCAI 17][Huawei] DeepFM_A Factorization-Machine based Neural Network for CTR Prediction.pdf b/CTR/[DeepFM][IJCAI 17][Huawei] DeepFM_A Factorization-Machine based Neural Network for CTR Prediction.pdf
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--- a/README.md
+++ b/README.md
@@ -1,8 +1,6 @@
 # 深度推荐系统与CTR预估工业界相关论文、业界分享
-动态更新推荐、广告工业界经典以及最前沿的论文、业界分享集合。所有资料均整理来自于互联网，如有侵权，请联系小助手deepdeliver。同时欢迎对推荐、广告方面工业界感兴趣的小伙伴添加小助手，将自动拉入交流群：
-<div align=left>
-<img width="200" src="avatar_wx.jpg" alt="交流群"/>
-</div>
+动态更新推荐、广告工业界经典以及最前沿的论文、业界分享集合。所有资料均整理来自于互联网。
+
 
 ### 其他相关资源
 * [王喆的推荐系统paper列表](https://github.com/wzhe06/Reco-papers)
@@ -72,4 +70,8 @@
 ## RL
 * [[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodology](https://github.com/imsheridan/DeepRec/blob/master/RL/%5BIJCAI%2019%5D%5BGoogle%5D%20Reinforcement%20Learning%20for%20Slate-based%20Recommender%20Systems_A%20Tractable%20Decomposition%20and%20Practical%20Methodology.pdf)
 * [[WSDM 19][Google] Top-K Off-Policy Correction for a REINFORCE Recommender System](https://github.com/imsheridan/DeepRec/blob/master/RL/%5BWSDM%2019%5D%5BGoogle%5D%20Top-K%20Off-Policy%20Correction%20for%20a%20REINFORCE%20Recommender%20System.pdf)
-* [[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation](https://github.com/imsheridan/DeepRec/blob/master/RL/%5BDRN%5D%5BWWW%2018%5D%5BMicrosoft%5D%20DRN_A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation.pdf)
\ No newline at end of file
+* [[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation](https://github.com/imsheridan/DeepRec/blob/master/RL/%5BDRN%5D%5BWWW%2018%5D%5BMicrosoft%5D%20DRN_A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation.pdf)
+
+# Acknowledgment
+This repository is implemented based on [DeepRec](https://github.com/imsheridan/DeepRec). 
+
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