深度推荐系统与CTR预估工业界相关论文、业界分享

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其他相关资源

• 王喆的推荐系统paper列表
• 王喆的广告paper列表

论文脉络

目录

CTR

• [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

Match

• [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

Ranking

• [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

Embedding

• [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

MTL

• [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

Diversity

• [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

EE

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

RL

• [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