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Title: GitHub - loganXcode/DeepRec: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

Open Graph Title: GitHub - loganXcode/DeepRec: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

X Title: GitHub - loganXcode/DeepRec: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

Description: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction - loganXcode/DeepRec

Open Graph Description: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction - loganXcode/DeepRec

X Description: 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction - loganXcode/DeepRec

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https://patch-diff.githubusercontent.com/loganXcode/DeepRec#深度推荐系统与ctr预估工业界相关论文业界分享
https://patch-diff.githubusercontent.com/loganXcode/DeepRec/blob/master/deepdeliver_group.png
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#其他相关资源
王喆的推荐系统paper列表https://github.com/wzhe06/Reco-papers
王喆的广告paper列表https://github.com/wzhe06/Ad-papers
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#论文脉络
https://patch-diff.githubusercontent.com/loganXcode/DeepRec/blob/master/deeprec_paper_structure.jpg
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#目录
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#ctr
[FiBiNET][RecSys 19][Weibo] FiBiNET_Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BFiBiNET%5D%5BRecSys%2019%5D%5BWeibo%5D%20FiBiNET_Combining%20Feature%20Importance%20and%20Bilinear%20feature%20Interaction%20for%20Click-Through%20Rate%20Prediction.pdf
[DSIN][IJCAI 19][Alibaba] Deep Session Interest Network for Click-Through Rate Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BDSIN%5D%5BIJCAI%2019%5D%5BAlibaba%5D%20Deep%20Session%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
[FGCNN][WWW 19][Huawei] Feature Generation by Convolutional Neural Network for Click-Through Rate Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BFGCNN%5D%5BWWW%2019%5D%5BHuawei%5D%20Feature%20Generation%20by%20Convolutional%20Neural%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
[AutoInt][CIKM 19] AutoInt_Automatic Feature Interaction Learning via Self-Attentive Neural Networkshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BAutoInt%5D%5BCIKM%2019%5D%20AutoInt_Automatic%20Feature%20Interaction%20Learning%20via%20Self-Attentive%20Neural%20Networks.pdf
[DIEN][AAAI 19][Alibaba] Deep Interest Evolution Network for Click-Through Rate Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BDIEN%5D%5BAAAI%2019%5D%5BAlibaba%5D%20Deep%20Interest%20Evolution%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
[PNN][TOIS 18] Product-based Neural Networks for User Response Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BPNN%5D%5BTOIS%2018%5D%20Product-based%20Neural%20Networks%20for%20User%20Response%20Prediction.pdf
[xDeepFM][KDD 18][Microsoft] xDeepFM_Combining Explicit and Implicit Feature Interactions for Recommender Systemshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BxDeepFM%5D%5BKDD%2018%5D%5BMicrosoft%5D%20xDeepFM_Combining%20Explicit%20and%20Implicit%20Feature%20Interactions%20for%20Recommender%20Systems.pdf
[DCN][KDD 17][Google] Deep & Cross Network for Ad Click Predictionshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BDCN%5D%5BKDD%2017%5D%5BGoogle%5D%20Deep%20%26%20Cross%20Network%20for%20Ad%20Click%20Predictions.pdf
[DIN][KDD 18][Alibaba] Deep Interest Network for Click-Through Rate Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BDIN%5D%5BKDD%2018%5D%5BAlibaba%5D%20Deep%20Interest%20Network%20for%20Click-Through%20Rate%20Prediction.pdf
[FNN][ECIR 16] Deep Learning over Multi-field Categorical Data_A Case Study on User Response Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BFNN%5D%5BECIR%2016%5D%20Deep%20Learning%20over%20Multi-field%20Categorical%20Data_A%20Case%20Study%20on%20User%20Response%20Prediction.pdf
[AFM][IJCAI 17] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networkshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BAFM%5D%5BIJCAI%2017%5D%20Attentional%20Factorization%20Machines%20-%20Learning%20the%20Weight%20of%20Feature%20Interactions%20via%20Attention%20Networks.pdf
[DeepFM][IJCAI 17][Huawei] DeepFM_A Factorization-Machine based Neural Network for CTR Predictionhttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BDeepFM%5D%5BIJCAI%2017%5D%5BHuawei%5D%20DeepFM_A%20Factorization-Machine%20based%20Neural%20Network%20for%20CTR%20Prediction.pdf
[NFM][SIGIR 17] Neural Factorization Machines for Sparse Predictive Analyticshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BNFM%5D%5BSIGIR%2017%5D%20Neural%20Factorization%20Machines%20for%20Sparse%20Predictive%20Analytics.pdf
[WDL][DLRS 16][Google] Wide & Deep Learning for Recommender Systemshttps://github.com/imsheridan/DeepRec/blob/master/CTR/%5BWDL%5D%5BDLRS%2016%5D%5BGoogle%5D%20Wide%20%26%20Deep%20Learning%20for%20Recommender%20Systems.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#match
[JTM][NIPS 19] Joint Optimization of Tree-based Index and Deep Model for Recommender Systemshttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BJTM%5D%5BNIPS%2019%5D%20Joint%20Optimization%20of%20Tree-based%20Index%20and%20Deep%20Model%20for%20Recommender%20Systems.pdf
[MIND][arxiv 19][Alibaba] Multi-Interest Network with Dynamic Routing for Recommendation at Tmallhttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BMIND%5D%5Barxiv%2019%5D%5BAlibaba%5D%20Multi-Interest%20Network%20with%20Dynamic%20Routing%20for%20Recommendation%20at%20Tmall.pdf
[SDM][CIKM 19][Alibaba] Sequential Deep Matching Model for Online Large-scale Recommender Systemhttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BSDM%5D%5BCIKM%2019%5D%5BAlibaba%5D%20Sequential%20Deep%20Matching%20Model%20for%20Online%20Large-scale%20Recommender%20System.pdf
[TDM][KDD 18][Alibaba] Learning Tree-based Deep Model for Recommender Systemshttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BTDM%5D%5BKDD%2018%5D%5BAlibaba%5D%20Learning%20Tree-based%20Deep%20Model%20for%20Recommender%20Systems.pdf
[NCF][WWW 17] Neural Collaborative Filteringhttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BNCF%5D%5BWWW%2017%5D%20Neural%20Collaborative%20Filtering.pdf
[YoutubeDNN][RecSys 16][Google] Deep Neural Networks for YouTube Recommendationshttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BYoutubeDNN%5D%5BRecSys%2016%5D%5BGoogle%5D%20Deep%20Neural%20Networks%20for%20YouTube%20Recommendations.pdf
[DSSM][CIKM 13][Microsoft] Learning Deep Structured Semantic Models for Web Search using Clickthrough Datahttps://github.com/imsheridan/DeepRec/blob/master/Match/%5BDSSM%5D%5BCIKM%2013%5D%5BMicrosoft%5D%20Learning%20Deep%20Structured%20Semantic%20Models%20for%20Web%20Search%20using%20Clickthrough%20Data.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#ranking
[PRM][RecSys 19][Alibaba] Personalized Re-ranking for Recommendationhttps://github.com/imsheridan/DeepRec/blob/master/Ranking/%5BPRM%5D%5BRecSys%2019%5D%5BAlibaba%5D%20Personalized%20Re-ranking%20for%20Recommendation.pdf
[BERT4Rec][CIKM 19][Alibaba] BERT4Rec_Sequential Recommendation with Bidirectional Encoder Representations from Transformerhttps://github.com/imsheridan/DeepRec/blob/master/Ranking/%5BBERT4Rec%5D%5BCIKM%2019%5D%5BAlibaba%5D%20BERT4Rec_Sequential%20Recommendation%20with%20Bidirectional%20Encoder%20Representations%20from%20Transformer.pdf
[BST][DLP-KDD 19][Alibaba] Behavior Sequence Transformer for E-commerce Recommendation in Alibabahttps://github.com/imsheridan/DeepRec/blob/master/Ranking/%5BBST%5D%5BDLP-KDD%2019%5D%5BAlibaba%5D%20Behavior%20Sequence%20Transformer%20for%20E-commerce%20Recommendation%20in%20Alibaba.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#embedding
[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnbhttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BAirbnb%20Embedding%5D%5BKDD%2018%5D%5BAirbnb%5D%20Real-time%20Personalization%20using%20Embeddings%20for%20Search%20Ranking%20at%20Airbnb.pdf
[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabahttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BAlibaba%20Embedding%5D%5BKDD%2018%5D%5BAlibaba%5D%20Billion-scale%20Commodity%20Embedding%20for%20E-commerce%20Recommendation%20in%20Alibaba.pdf
[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representationshttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BDeepWalk%5D%5BKDD%2014%5D%20DeepWalk-%20Online%20Learning%20of%20Social%20Representations.pdf
[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embeddinghttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BLINE%5D%5BWWW%2015%5D%5BMicrosoft%5D%20LINE_Large-scale%20Information%20Network%20Embedding.pdf
[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networkshttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BNode2vec%5D%5BKDD%2016%5D%20Node2vec_Scalable%20Feature%20Learning%20for%20Networks.pdf
[SDNE][KDD 16] Structural Deep Network Embeddinghttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BSDNE%5D%5BKDD%2016%5D%20Structural%20Deep%20Network%20Embedding.pdf
[Struc2Vec][KDD 17]struc2vec_Learning Node Representations from Structural Identityhttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BStruc2Vec%5D%5BKDD%2017%5Dstruc2vec_Learning%20Node%20Representations%20from%20Structural%20Identity.pdf
[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphshttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BGraphSAGE%5D%5BNIPS%2017%5D%20Inductive%20Representation%20Learning%20on%20Large%20Graphs.pdf
[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networkshttps://github.com/imsheridan/DeepRec/blob/master/Embedding/%5BGCN%5D%5BICLR%2017%5D%20Semi-supervised%20Classification%20with%20Graph%20Convolutional%20Networks.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#mtl
[RecSys 19][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendationhttps://github.com/imsheridan/DeepRec/blob/master/MTL/%5BRecSys%2019%5D%5BAlibaba%5D%20A%20Pareto-Efficient%20Algorithm%20for%20Multiple%20Objective%20Optimization%20in%20E-Commerce%20Recommendation.pdf
[MMoE][KDD 18][Google] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Expertshttps://github.com/imsheridan/DeepRec/blob/master/MTL/%5BMMoE%5D%5BKDD%2018%5D%5BGoogle%5D%20Modeling%20Task%20Relationships%20in%20Multi-task%20Learning%20with%20Multi-gate%20Mixture-of-Experts.pdf
[ESMM][SIGIR 18][Alibaba] Entire Space Multi-Task Model_An Effective Approach for Estimating Post-Click Conversion Ratehttps://github.com/imsheridan/DeepRec/blob/master/MTL/%5BESMM%5D%5BSIGIR%2018%5D%5BAlibaba%5D%20Entire%20Space%20Multi-Task%20Model_An%20Effective%20Approach%20for%20Estimating%20Post-Click%20Conversion%20Rate.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#diversity
[CIKM 18][Google] Practical Diversified Recommendations on YouTube with Determinantal Point Processeshttps://github.com/imsheridan/DeepRec/blob/master/Diversity/%5BCIKM%2018%5D%5BGoogle%5D%20Practical%20Diversified%20Recommendations%20on%20YouTube%20with%20Determinantal%20Point%20Processes.pdf
[NeurIPS 18][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversityhttps://github.com/imsheridan/DeepRec/blob/master/Diversity/%5BNeurIPS%2018%5D%5BHulu%5D%20Fast%20Greedy%20MAP%20Inference%20for%20Determinantal%20Point%20Process%20to%20Improve%20Recommendation%20Diversity.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#ee
[LinUCB][WWW 10][Yahoo] A Contextual-Bandit Approach to Personalized News Article Recommendationhttps://github.com/imsheridan/DeepRec/blob/master/EE/%5BLinUCB%5D%5BWWW%2010%5D%5BYahoo%5D%20A%20Contextual-Bandit%20Approach%20to%20Personalized%20News%20Article%20Recommendation.pdf
https://patch-diff.githubusercontent.com/loganXcode/DeepRec#rl
[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodologyhttps://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 Systemhttps://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 Recommendationhttps://github.com/imsheridan/DeepRec/blob/master/RL/%5BDRN%5D%5BWWW%2018%5D%5BMicrosoft%5D%20DRN_A%20Deep%20Reinforcement%20Learning%20Framework%20for%20News%20Recommendation.pdf
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