推荐、广告工业界经典以及最前沿的论文、资料集合

02-12 19:11

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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

Addressing Marketing Bias in Product Recommendations.pdf

BERT6个子项

Transformer15个子项

Adaptive Attention Span in Transformers.pdf

Augmenting Self-attention with Persistent Memory.pdf

BP-Transformer_Modelling Long-Range Context via Binary Partitioning.pdf

Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling.pdf

DiSAN_Directional Self-Attention Network for RNN_CNN-Free Language Understanding.pdf

Generating Long Sequences with Sparse Transformers.pdf

Insertion-based Decoding with automatically Inferred Generation Order.pdf

Large Memory Layers with Product Keys.pdf

Levenshtein Transformer.pdf

Reformer_The Efficient Transformer.pdf

Self-Attention with Relative Position Representations.pdf

Star-Transformer.pdf

Tensorized Embedding Layers for Efficient Model Compression.pdf

Transformer-XL_Attentive Language Models Beyond a Fixed-Length Context.pdf

Universal Transformers.pdf

[ALBERT][arxiv 19][Google] ALBERT_A Lite BERT for Self-supervised Learning of Language Representations.pdf

[BERT][arxiv 19][Google ]BERT_Pre-training of Deep Bidirectional Transformers for Language Understanding.pdf

[ERNIE][arxiv 19][Baidu] ERNIE_Enhanced Representation through Knowledge Integration.pdf

[T5][arxiv 19][Google] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.pdf

[XLNet][arxiv 19][Google] XLNet_Generalized Autoregressive Pretraining for Language Understanding.pdf

CTR15个子项

Extend15个子项

CAN_Revisiting Feature Co-Action for Click-Through Rate Prediction.pdf

DCN-M_Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.pdf

FuxiCTR_An Open Benchmark for Click-Through Rate Prediction.pdf

[AAAI 20][Alibaba] Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.pdf

[AAAI 20][Alibaba] Rocket Launching_A Universal and Efficient Framework for Training Well-performing Light Net.pdf

[Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction.pdf

[AutoFIS][KDD 20][Huawei] AutoFIS_Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction.pdf

[DMR][AAAI 20][Alibaba] Deep Match to Rank Model for Personalized Click-Through Rate Prediction.pdf

[KDD 19][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.pdf

[MARN][WWW 20][Alibaba] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction.pdf

[MOBUIS][KDD 19] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search.pdf

[PAL][RecSys 19][Huawei] PAL_A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems.pdf

[PQR][AAAI 20][Tencent] Projective Quadratic Regression for Online Learning.pdf

[SIGIR 20][Alibaba] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction.pdf

[WWW 20][Tencent]Field-aware Calibration_A Simple and Empirically Strong Method for Reliable Probabilistic Predictions.pdf

[AFM][IJCAI 17] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks.pdf

[AutoInt][CIKM 19] AutoInt_Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf

[DCN][KDD 17][Google] Deep & Cross Network for Ad Click Predictions.pdf

[DIEN][AAAI 19][Alibaba] Deep Interest Evolution Network for Click-Through Rate Prediction.pdf

[DIN][KDD 18][Alibaba] Deep Interest Network for Click-Through Rate Prediction.pdf

[DSIN][IJCAI 19][Alibaba] Deep Session Interest Network for Click-Through Rate Prediction.pdf

[DeepFM][IJCAI 17][Huawei] DeepFM_A Factorization-Machine based Neural Network for CTR Prediction.pdf

[FGCNN][WWW 19][Huawei] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction.pdf

[FNN][ECIR 16] Deep Learning over Multi-field Categorical Data_A Case Study on User Response Prediction.pdf

[FiBiNET][RecSys 19][Weibo] FiBiNET_Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf

[NFM][SIGIR 17] Neural Factorization Machines for Sparse Predictive Analytics.pdf

[PNN][TOIS 18] Product-based Neural Networks for User Response Prediction.pdf

[WDL][DLRS 16][Google] Wide & Deep Learning for Recommender Systems.pdf

[xDeepFM][KDD 18][Microsoft] xDeepFM_Combining Explicit and Implicit Feature Interactions for Recommender Systems.pdf

Diversity3个子项

[AAAI 20][Google] Pairwise Fairness for Ranking and Regression.pdf

[CIKM 18][Google] Practical Diversified Recommendations on YouTube with Determinantal Point Processes.pdf

[NeurIPS 18][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity.pdf

EE1个子项

[LinUCB][WWW 10][Yahoo] A Contextual-Bandit Approach to Personalized News Article Recommendation.pdf

Embedding14个子项

Extend2个子项

[MGQE][WWW 20][Google] Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems.pdf

[SSL][Google] Self-supervised Learning for Deep Models in Recommendations.pdf

[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf

[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf

[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations.pdf

[Etsy Embedding][DAPA-WSDM 19] Learning Item-Interaction Embeddings for User Recommendations.pdf

[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf

[KDD 20][FaceBook] Embedding-based Retrieval in Facebook Search.pdf

[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks.pdf

[Word2Vec][2013][Google] Efficient Estimation of Word Representations in Vector Space.pdf

[Word2Vec][NIPS 13][Google] Distributed Representations of Words and Phrases and their Compositionality.pdf

Graph2个子项

Embedding11个子项

[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding.pdf

[SDNE][KDD 16] Structural Deep Network Embedding.pdf

[Struc2Vec][KDD 17] Struc2Vec_Learning Node Representations from Structural Identity.pdf

Extend1个子项

[WSDM 20][Tencent] Initialization for Network Embedding_A Graph Partition.pdf

[DeepWak][KDD 14] DeepWalk_Online Learning of Social Representations.pdf

[GraRep][CIKM 15] GraRep_Learning Graph Representations with Global Structural Information.pdf

[HOPE][KDD 16] Asymmetric Transitivity Preserving Graph Embedding.pdf

[NetMF][WSDM 18] Network Embedding as Matrix Factorization_Unifying DeepWalk, LINE, PTE, and node2vec.pdf

[NetSMF][WWW 19] NetSMF_Large-Scale Network Embedding as Sparse Matrix.pdf

[Node2Vec][KDD 16] Node2Vec_Scalable Feature Learning for Networks.pdf

[ProNE][IJCAI 19] ProNE_Fast and Scalable Network Representation Learning.pdf

NN3个子项

Extend4个子项

[GCN][ICLR 17] Semi-Supervised Classification with Graph Convolutional Networks.pdf

[FastGCN][ICLR 18] FastGCN_Fast Learning with Graph Convolutional Networks via Importance Sampling.pdf

[GAT][ICLR 18] Graph Attention Networks.pdf

[KDD 20][Alibaba] Understanding Negative Sampling in Graph Representation Learning.pdf

[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs.pdf

[PinSage][KDD 18][Pinterest] Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf

KG3个子项

Extend6个子项

[AAAI 19][Ebay] Explainable Reasoning over Knowledge Graphs for Recommendation.pdf

[ATBRG][SIGIR 20][Alibaba] ATBRG_Adaptive Target-Behavior Relational Graph Network for Effective Recommendation.pdf

[DLP-KDD 19] An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation.pdf

[KDD 19][Meituan] Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems.pdf

[WWW 19][Microsoft] Knowledge Graph Convolutional Networks for Recommender Systems.pdf

[WWW 19][Microsoft] Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation.pdf

[KGAT][KDD 19] KGAT_Knowledge Graph Attention Network for Recommendation.pdf

[RippleNet][CIKM 18] RippleNet_Propagating User Preferences on the Knowledge Graph for Recommender Systems.pdf

KeyNote2个子项

[2019] DLRM_纪厚业.pdf

[2020] HGNN_纪厚业.pdf

MTL7个子项

Extend5个子项

Perceive Your Users in Depth- Learning Universal User Representations from Multiple E-commerce Tasks.pdf

[ESM2][arxiv][Alibaba] Conversion Rate Prediction via Post-Click Behaviour Modeling.pdf

[KDD 20][Alibaba] M2GRL_A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems.pdf

[KDD 20][Alibaba] Multi-objective Optimization for Guaranteed Delivery in Video Service Platform.pdf

[RecSys 19][Google] Recommending What Video to Watch Next_A Multitask Ranking System.pdf

[AAAI 19][Google] SNR_Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning.pdf

[AAAI 20] Learning Sparse Sharing Architectures for Multiple Tasks.pdf

[ESMM][SIGIR 18][Alibaba] Entire Space Multi-Task Model_An Effective Approach for Estimating Post-Click Conversion Rate.pdf

[KDD 20][Google] Multitask Mixture of Sequential Experts for User Activity Streams.pdf

[MMoE][KDD 18][Google] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.pdf

[RecSys 19][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation.pdf

Match10个子项

Extend9个子项

Next Item Recommendation with Self-Attention.pdf

[CDL][KDD 15] Collaborative Deep Learning for Recommender Systems.pdf

[LightRec][WWW 20][Microsoft]LightRec_a Memory and Search-Efficient Recommender System.pdf

[MGNN][WWW 20][Tencent] Beyond Clicks_Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction.pdf

[MOBIUS][KDD 19][Baidu] MOBIUS_Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search.pdf

[RecSys 19][Google] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations.pdf

[SIF][WWW 20][4Paradigm] Efficient Neural Interaction Function Search for Collaborative Filtering.pdf

[DR][ByteDance] Deep Retrieval_An End-to-End Learnable Structure Model for Large-Scale Recommendations.pdf

[DSSM][CIKM 13][Microsoft] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data.pdf

[JTM][NIPS 19] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems.pdf

[MIND][arxiv 19][Alibaba] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.pdf

[NCF][WWW 17] Neural Collaborative Filtering.pdf

[SDM][CIKM 19][Alibaba] Sequential Deep Matching Model for Online Large-scale Recommender System.pdf

[TDM][KDD 18][Alibaba] Learning Tree-based Deep Model for Recommender Systems.pdf

[YoutubeDNN][RecSys 16][Google] Deep Neural Networks for YouTube Recommendations.pdf

deep learning for matching in search and recommendation book 2020.pdf

RL5个子项

Extend9个子项

[AAAI 19][Huawei] Large-scale Interactive Recommendation with Tree-structured Policy Gradient.pdf

[AAAI 20][ByteDance] Deep Reinforcement Learning for Online Advertising in Recommender Systems.pdf

[AAAI19][Alibaba] Virtual Taobao_Virtualizing Real-world Online Retail Environment for Reinforcement Learning.pdf

[CIKM 18][Alibaba] Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising.pdf

[HATCH][WWW 20][Didi]Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation.pdf

[ICML 19][Alibaba] Generative Adversarial User Model for Reinforcement Learning Based Recommendation System.pdf

[KDD 19][JD] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems.pdf

[RecSys18][JD] Deep Reinforcement Learning for Page-wise Recommendations.pdf

[arxiv][ByteDance] Jointly Learning to Recommend and Advertise.pdf

[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation.pdf

[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodology.pdf

[WSDM 19][Google] Top-K Off-Policy Correction for a REINFORCE Recommender System.pdf

[WWW 20][Google] Off-policy Learning in Two-stage Recommender Systems.pdf

Ranking10个子项

[ACL 19][Microsoft] Neural News Recommendation with Long- and Short-term User Representations.pdf

[BERT4Rec][CIKM 19][Alibaba] BERT4Rec_Sequential Recommendation with Bidirectional Encoder Representations from Transformer.pdf

[BST][DLP-KDD 19][Alibaba] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba.pdf

[Choppy][SIGIR 20][Google] Choppy- Cut Transformer For Ranked List Truncation.pdf

[FTRL][KDD 13][Google] Ad Click Prediction_a View from the Trenches.pdf

[GBDT+LR][ADKDD 14][Facebook] Practical Lessons from Predicting Clicks on Ads at Facebook.pdf

[ImageCTR][CIKM 18][Alibaba] Image Matters_Visually modeling user behaviors using Advanced Model Server.pdf

[MDM 19] Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks.pdf

[SIGIR 20][Google] Feature Transformation for Neural Ranking Models.pdf

[arxiv 18][Facebook] Collaborative Multi-modal Deep Learning for The Personalized Product Retrieval in Facebook Marketplace.pdf

ReRank2个子项

[PRM][RecSys 19][Alibaba] Personalized Re-ranking for Recommendation.pdf

[SIGIR 18] Learning a Deep Listwise Context Model for Ranking Refinement.pdf

Sequential5个子项

[Transformer][ICDM 18] Self-Attentive Sequential Recommendation.pdf

Extend4个子项

[CMI][KDD 20][Alibaba] Controllable Multi-Interest Framework for Recommendation.pdf

[SIGIR 20][Tencent] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.pdf

[SIGIR 19][Alibaba] Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction.pdf

[CNN][WSDM 18] Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.pdf

[GRU][CIKM 18] Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.pdf

[IJCAI 19] Sequential Recommender Systems_Challenges, Progress and Prospects.pdf

UserModel1个子项

[KDD 19][Tencent] A User-Centered Concept Mining System for Query and Document Understanding at Tencent.pdf

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