gongqingyi / Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer and Reinforcement Learning.

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer and Reinforcement Learning.

0_New_Papers_in_2020

• 2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search
  
• 2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search
  
• 2020 (Alibaba) (Arxiv) [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
  
• 2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search
  
• 2020 (Alibaba) (KDD) **[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations
  
• 2020 (Alibaba) (KDD) *[ComiRec] Controllable Multi-Interest Framework for Recommendation
  
• 2020 (Alibaba) (SIGIR) [ATBRG] ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
  
• 2020 (Alibaba) (SIGIR) [DHAN] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
  
• 2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
  
• 2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
  
• 2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
  
• 2020 (Baidu) (CIKM) [DeepChain] Whole-Chain Recommendations
  
• 2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising
  
• 2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise
  
• 2020 (Facebook) (KDD) **[Embedding for Facebook Search] Embedding-based Retrieval in Facebook Search
  
• 2020 (Google) (Arxiv) Self-supervised Learning for Large-scale Item Recommendations
  
• 2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive
  
• 2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams
  
• 2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  
• 2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
  
• 2020 (JD) (SIGIR) [DPSR] Towards Personalized and Semantic Retrieval - An End-to-EndSolution for E-commerce Search via Embedding Learning
  
• 2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering
  
• 2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems
  
• 2020 (Kuaishou) (SIGIR) [SML] How to Retrain Recommender System? A Sequential Meta-Learning Method
  
• 2020 (Tencent) (Recsys) [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
  
• 2020 (Tencent) (SIGIR) [PeterRec] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
  

1_Embedding

• 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
  
• 2014 (KDD) [DeepWalk] DeepWalk - online learning of social representations
  
• 2015 (WWW) [LINE] LINE Large-scale Information Network Embedding
  
• 2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks
  
• 2017 (ICLR) [GCN] Semi-supervised Classification with Graph Convolutional Networks
  
• 2017 (KDD) [Struc2vec] struc2vec - Learning Node Representations from Structural Identity
  
• 2017 (NIPS) [GraphSAGE] Inductive Representation Learning on Large Graphs
  
• 2018 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb
  
• 2018 (Alibaba) (KDD) *[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
  
• 2018 (ICLR) [GAT] Graph Attention Networks
  
• 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  
• 2018 (WSDM) [NetMF] Network embedding as matrix factorization - Unifying deepwalk, line, pte, and node2vec
  
• 2019 (Alibaba) (KDD) *[GATNE] Representation Learning for Attributed Multiplex Heterogeneous Network
  

2_Matching

• 2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data
  
• 2015 (KDD) [Sceptre] Inferring Networks of Substitutable and Complementary Products
  
• 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
  
• 2018 (KDD) *[TDM] (Alibaba) Learning Tree-based Deep Model for Recommender Systems
  
• 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  
• 2019 (Alibaba) (CIKM) **[MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
  
• 2019 (Alibaba) (CIKM) *[SDM] SDM - Sequential deep matching model for online large-scale recommender system
  
• 2019 (Alibaba) (NIPS) *[JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
  
• 2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
  
• 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
  
• 2019 [Tencent] (KDD) A User-Centered Concept Mining System for Query and Document Understanding at Tencent
  
• 2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search
  
• 2020 (Alibaba) (KDD) *[ComiRec] Controllable Multi-Interest Framework for Recommendation
  
• 2020 (Facebook) (KDD) **[Embedding for Facebook Search] Embedding-based Retrieval in Facebook Search
  
• 2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
  
• 2020 (JD) (SIGIR) [DPSR] Towards Personalized and Semantic Retrieval - An End-to-EndSolution for E-commerce Search via Embedding Learning
  

3_Ranking

• 2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook
  
• 2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems
  
• 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
  
• 2018 (Alibaba) (CIKM) [Image CTR] Image Matters - Visually Modeling User Behaviors Using Advanced Model Server
  
• 2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction
  
• 2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
  
• 2019 (Alibaba) (IJCAI) [DSIN] Deep Session Interest Network for Click-Through Rate Prediction
  
• 2019 (Alibaba) (IJCAI) [DeepMCP] Representation Learning-Assisted Click-Through Rate Prediction
  
• 2019 (Alibaba) (KDD) [DSTN] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
  
• 2019 (Alibaba) (KDD) [MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
  
• 2019 (Alibaba) (KDD)[BST] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
  
• 2019 (Alibaba) (WWW) [TiSSA] TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors
  
• 2019 (Facebook) (Arxiv) [DLRM] (Facebook) Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook
  
• 2019 (KDD) (Airbnb) Applying Deep Learning To Airbnb Search
  
• 2019 (SIGIR) [BERT4Rec] (Alibaba) (SIGIR2019) BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer
  
• 2019 (Tencent) (KDD) [RALM] TReal-time Attention Based Look-alike Model for Recommender System
  
• 2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search
  
• 2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
  
• 2020 (Alibaba) (Arxiv) [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
  
• 2020 (Alibaba) (SIGIR) [DHAN] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
  
• 2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising
  
• 2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive
  
• 2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  
• 2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems
  

Classic

• 2003 (Amazon) (IEEE) [CF] Amazon.com recommendations - Item-to-item collaborative filtering
  
• 2009 (Computer) [MF] Matrix factorization techniques for recommender systems
  
• 2010 (ICDM) [FM] Factorization machines
  
• 2016 (ICLR) [GRU4Rec] Session-based Recommendations with Recurrent Neural Networks
  
• 2017 (Amazon) (IEEE) Two decades of recommender systems at Amazon.com
  

Cross

• 2016 (ECIR) [FNN] Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction
  
• 2016 (KDD) [Deepintent] Deepintent - Learning attentions for online advertising with recurrent neural networks
  
• 2016 (Microsoft) (KDD) [Deep Crossing] Deep Crossing - Web-scale modeling without manually crafted combinatorial features
  
• 2017 (ADKDD)[DCN] Deep & CrossNetwork for Ad Click Predictions
  
• 2017 (Huawei ) (IJCAI) [DeepFM] DeepFM - A Factorization-Machine based Neural Network for CTR Prediction
  
• 2017 (IJCAI) [AFM] Attentional Factorization Machines Learning the Weight of Feature Interactions via Attention Networks
  
• 2017 (SIGIR) [NFM] Neural Factorization Machines for Sparse Predictive Analytics
  
• 2017 (WWW) [NCF] Neural Collaborative Filtering
  
• 2018 (KDD) [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems
  
• 2018 (TOIS) [PIN] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
  
• 2018 (WSDM) [Latent Cross] Latent Cross Making Use of Context in Recurrent Recommender Systems
  
• 2019 (CIKM) [AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks
  
• 2019 (Huawei) (WWW) [FGCNN] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
  
• 2019 (Tencent) (AAAI) [IFM] Interaction-aware Factorization Machines for Recommender Systems
  
• 2019 (Weibo) (Recsys) [FiBiNET] FiBiNET - combining feature importance and bilinear feature interaction for click-through rate prediction
  

Multi-task

• 2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer
  
• 2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
  
• 2018 (Alibaba) (SIGIR) [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate
  
• 2018 (Google) (KDD) [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
  
• 2019 (Google) (Recsys)[Youtube Multi-task] Recommending what video to watch next - a multitask ranking system
  
• 2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
  
• 2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams
  
• 2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  
• 2020 (Tencent) (Recsys) [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
  

4_Post-ranking

• 1998 (SIGIR) [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
  
• 2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation
  
• 2016 (Amazon) (RecSys) Adaptive, Personalized Diversity for Visual Discovery
  
• 2017 (Hulu) (NIPS) [DPP] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
  
• 2018 (Alibaba) (IJCAI) [Alibaba GMV] Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
  
• 2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes
  
• 2018 (SIGIR) [DLCM] Learning a Deep Listwise Context Model for Ranking Refinement
  
• 2019 (Alibaba) (WWW) [Value-based RL] Value-aware Recommendation based on Reinforcement Profit Maximization
  
• 2019 (Alibaba) (KDD) [GAttN] Exact-K Recommendation via Maximal Clique Optimization
  
• 2019 (Alibaba) (RecSys) [PRM] Personalized Re-ranking for Recommendation
  
• 2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
  
• 2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search
  
• 2020 (Alibaba) (CIKM) [EdgeRec] EdgeRec - Recommender System on Edge in Mobile Taobao
  

Seq2Slate

• 2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces
  
• 2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
  
• 2017 (KDD) [DCM] Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction
  
• 2018 (Microsoft) (EMNLP) [RL4NMT] A study of reinforcement learning for neural machine translation
  
• 2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns
  

5_Graph_Neural_Networks

• 2018 (Pinterest) (KDD) [PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  
• 2019 (Alibaba) (KDD) [IntentGC] IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
  
• 2019 (Alibaba) (KDD) [MEIRec] Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
  
• 2019 (Alibaba) (SIGIR) [GIN] Graph Intention Network for Click-through Rate Prediction in Sponsored Search
  
• 2020 (Alibaba) (SIGIR) [ATBRG] ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
  

6_Transfer_Learning

• 2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network
  
• 2015 (ICLR) [Fitnets] Fitnets - Hints for thin deep nets
  
• 2018 (Alibaba) (AAAI) [Rocket] Rocket launching - A universal and efficient framework for training well-performing light net
  
• 2018 (KDD)[Ranking Distillation] Ranking distillation - Learning compact ranking models with high performance for recommender system
  
• 2019 (ICCV) [RCO] Knowledge Distillation via Route Constrained Optimization
  
• 2020 (Alibaba) (KDD) *[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations
  

Cross-domain

• 2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
  
• 2016 (JMLR) Domain-Adversarial Training of Neural Networks
  
• 2018 (CIKM) CoNet - Collaborative Cross Networks for Cross-Domain Recommendation
  
• 2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search
  
• 2019 (Alibaba) (KDD) [MGTL] A minimax game for instance based selective transfer learning
  
• 2019 (CIKM) DTCDR - A Framework for Dual-Target Cross-Domain Recommendation
  
• 2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
  
• 2020 (WSDM) DDTCDR - Deep Dual Transfer Cross Domain Recommendation
  

Meta-Learning

• 2019 (Alibaba) (KDD) [s_2Meta] Sequential Scenario-Specific Meta Learner for Online Recommendation
  
• 2020 (Kuaishou) (SIGIR) [SML] How to Retrain Recommender System? A Sequential Meta-Learning Method
  

Transfer

• 2018 (CVPR) Efficient parametrization of multi-domain deep neural networks
  
• 2019 (ICML) Parameter-efficient transfer learning for NLP
  
• 2020 (Tencent) (SIGIR) [PeterRec] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
  

7_Reinforcement_Learning

• 2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation
  
• 2018 (Alibaba) (KDD) Reinforcement Learning to Rank in E-Commerce Search Engine Formalization, Analysis, and Application
  
• 2018 (Alibaba) (WWW) [MA-RDPG] Learning to Collaborate Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
  
• 2018 (JD) (KDD) [DEERS] Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
  
• 2018 (JD) (RecSys) [DeepPage] Deep Reinforcement Learning for Page-wise Recommendations
  
• 2018 (KDD) [Robust DQN] Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation
  
• 2018 (Spotify) (Recsys) [Spotify Bandit] Explore, Exploit, and Explain Personalizing Explainable Recommendations with Bandits
  
• 2018 [Microsoft] (WWW) [DRN] DRN - A Deep Reinforcement Learning Framework for News Recommendation
  
• 2019 (Alibaba) (WWW) [HRL] Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
  
• 2019 (DRL4KDD) [LIRD] Deep Reinforcement Learning for List-wise Recommendations
  
• 2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
  
• 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
  
• 2019 (JD) (KDD) [FeedRec] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
  
• 2019 (Sigweb) Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey
  
• 2020 (Baidu) (CIKM) [DeepChain] Whole-Chain Recommendations
  
• 2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise
  
• 2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering
  

8_Self_Supervised_Learning

• 2020 (Google) (Arxiv) Self-supervised Learning for Large-scale Item Recommendations