本资源整理了深度学习在推荐系统、广告系统中应用的一些经典论文，涉及推荐系统中召回、排序、CTR预估、Embedding化、系统多样性、多目标，排序和混排的EE和RL等部分。
资源整理自网络，源链接：https://github.com/imsheridan/DeepRec
目录
点击率预估
召回层
排序层
向量化
多任务学习
多样性
探索/应用（EE）
强化学习
序列模型推荐
用户模型
BERT推荐模型
图模型推荐(浅层/深层图模型)
点击率预估
•[FiBiNET][RecSys 19][Weibo] FiBiNET_Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
•[DSIN][IJCAI 19][Alibaba] Deep Session Interest Network for Click-Through Rate Prediction
•[FGCNN][WWW 19][Huawei] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
•[AutoInt][CIKM 19] AutoInt_Automatic Feature Interaction Learning via Self-Attentive Neural Networks
•[DIEN][AAAI 19][Alibaba] Deep Interest Evolution Network for Click-Through Rate Prediction
•[PNN][TOIS 18] Product-based Neural Networks for User Response Prediction
•[xDeepFM][KDD 18][Microsoft] xDeepFM_Combining Explicit and Implicit Feature Interactions for Recommender Systems
•[DCN][KDD 17][Google] Deep & Cross Network for Ad Click Predictions
•[DIN][KDD 18][Alibaba] Deep Interest Network for Click-Through Rate Prediction
•[FNN][ECIR 16] Deep Learning over Multi-field Categorical Data_A Case Study on User Response Prediction
•[AFM][IJCAI 17] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks
•[DeepFM][IJCAI 17][Huawei] DeepFM_A Factorization-Machine based Neural Network for CTR Prediction
•[NFM][SIGIR 17] Neural Factorization Machines for Sparse Predictive Analytics
•[WDL][DLRS 16][Google] Wide & Deep Learning for Recommender Systems
召回层
•[JTM][NIPS 19] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
•[MIND][arxiv 19][Alibaba] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
•[SDM][CIKM 19][Alibaba] Sequential Deep Matching Model for Online Large-scale Recommender System
•[TDM][KDD 18][Alibaba] Learning Tree-based Deep Model for Recommender Systems
•[NCF][WWW 17] Neural Collaborative Filtering
•[YoutubeDNN][RecSys 16][Google] Deep Neural Networks for YouTube Recommendations
•[DSSM][CIKM 13][Microsoft] Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
排序层
•[PRM][RecSys 19][Alibaba] Personalized Re-ranking for Recommendation
•[BERT4Rec][CIKM 19][Alibaba] BERT4Rec_Sequential Recommendation with Bidirectional Encoder Representations from Transformer
•[BST][DLP-KDD 19][Alibaba] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
向量化
•[Airbnb Embedding][KDD 18][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb
•[Alibaba Embedding][KDD 18][Alibaba] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
•[DeepWalk][KDD 14] DeepWalk- Online Learning of Social Representations
•[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding
•[Node2vec][KDD 16] Node2vec_Scalable Feature Learning for Networks
•[SDNE][KDD 16] Structural Deep Network Embedding
•[Struc2Vec][KDD 17]struc2vec_Learning Node Representations from Structural Identity
•[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs
•[GCN][ICLR 17] Semi-supervised Classification with Graph Convolutional Networks
多任务学习
•[RecSys 19][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation
•[MMoE][KDD 18][Google] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
•[ESMM][SIGIR 18][Alibaba] Entire Space Multi-Task Model_An Effective Approach for Estimating Post-Click Conversion Rate
多样性
•[CIKM 18][Google] Practical Diversified Recommendations on YouTube with Determinantal Point Processes
•[NeurIPS 18][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
探索/应用（EE）
•[LinUCB][WWW 10][Yahoo] A Contextual-Bandit Approach to Personalized News Article Recommendation
强化学习
•[IJCAI 19][Google] Reinforcement Learning for Slate-based Recommender Systems_A Tractable Decomposition and Practical Methodology
•[WSDM 19][Google] Top-K Off-Policy Correction for a REINFORCE Recommender System
•[DRN][WWW 18][Microsoft] DRN_A Deep Reinforcement Learning Framework for News Recommendation
序列模型推荐
•[IJCAI 19] Sequential Recommender Systems_Challenges, Progress and Prospects
用户模型
•[KDD 19][Tencent] A User-Centered Concept Mining System for Query and Document Understanding at Tencent
BERT推荐模型
•[ALBERT][arxiv 19][Google] ALBERT_A Lite BERT for Self-supervised Learning of Language Representations
•[BERT][arxiv 19][Google ]BERT_Pre-training of Deep Bidirectional Transformers for Language Understanding
•[ERNIE][arxiv 19][Baidu] ERNIE_Enhanced Representation through Knowledge Integration
•[T5][arxiv 19][Google] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
•[XLNet][arxiv 19][Google] XLNet_Generalized Autoregressive Pretraining for Language Understanding
图模型推荐
浅层图向量化模型
•[DeepWak][KDD 14] DeepWalk_Online Learning of Social Representations
•[GraRep][CIKM 15] GraRep_Learning Graph Representations with Global Structural Information
•[HOPE][KDD 16] Asymmetric Transitivity Preserving Graph Embedding
•[LINE][WWW 15][Microsoft] LINE_Large-scale Information Network Embedding
•[NetMF][WSDM 18] Network Embedding as Matrix Factorization_Unifying DeepWalk, LINE, PTE, and node2vec
•[NetSMF][WWW 19] NetSMF_Large-Scale Network Embedding as Sparse Matrix
•[Node2Vec][KDD 16] Node2Vec_Scalable Feature Learning for Networks
•[ProNE][IJCAI 19] ProNE_Fast and Scalable Network Representation Learning
•[SDNE][KDD 16] Structural Deep Network Embedding
•[Struc2Vec][KDD 17] Struc2Vec_Learning Node Representations from Structural Identity
图神经网络模型
•[FastGCN][ICLR 18] FastGCN_Fast Learning with Graph Convolutional Networks via Importance Sampling
•[GAT][ICLR 18] Graph Attention Networks
•[GCN][ICLR 17] Semi-Supervised Classification with Graph Convolutional Networks
•[GraphSAGE][NIPS 17] Inductive Representation Learning on Large Graphs
