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

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking (CTR/CVR prediction), Post-Ranking, Relevance-Ranking, LLM based ranking, Reinforcement Learning and so on.

Please cite our paper: Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives, which was published in TOIS 2026.

00_Overview

• 2026 (TOIS) [DLTR] Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising - An Overview and New Perspectives
  

01_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 (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
  

02_Matching

• 1994 (CSCW) [User-CF] GroupLens - An Open Architecture for Collaborative Filtering of Netnews
  
• 1998 (Microsoft) Empirical Analysis of Predictive Algorithms for Collaborative Filtering
  
• 2003 (Amazon) [Item-CF] Amazon.com recommendations - item-to-item collaborative filtering
  
• 2009 (Computer) [MF] Matrix factorization techniques for recommender systems
  
• 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 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb
  
• 2018 (Alibaba) (KDD) * [TDM] 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 (Amazon) (KDD) Semantic Product Search
  
• 2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search
  
• 2019 (Google) (RecSys) **[Two-Tower] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
  
• 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) (Arxiv) [SWING] Large Scale Product Graph Construction for Recommendation in E-commerce
  
• 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 (Google) (WWW) *[MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
  
• 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 (Microsoft) (Arxiv) TwinBERT - Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval
  
• 2021 (Alibaba) (KDD) * [MGDSPR] Embedding-based Product Retrieval in Taobao Search
  
• 2021 (Alibaba) (SIGIR) * [PDN] Path-based Deep Network for Candidate Item Matching in Recommenders
  
• 2021 (Amazon) (KDD) Extreme Multi-label Learning for Semantic Matching in Product Search
  
• 2021 (Baidu) (KDD) Pre-trained Language Model for Web-scale Retrieval in Baidu Search
  
• 2021 (Bytedance) (Arxiv) [DR] Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations
  
• 2021 (Meituan) (DLP-KDD) [DAT]A Dual Augmented Two-tower Model for Online Large-scale Recommendation
  
• 2022 (Alibaba) (CIKM) **[NANN] Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
  
• 2022 (Alibaba) (CIKM) [CLE-QR] Query Rewriting in TaoBao Search
  
• 2022 (Alibaba) **(CIKM) [MOPPR] Multi-Objective Personalized Product Retrieval in Taobao Search
  
• 2024 (Bytedance) (KDD) [Trinity] Trinity - Syncretizing Multi-:Long-Tail:Long-Term Interests All in One
  
• 2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations
  
• 2025 (Bytedance) (Arxiv) [LongRetriever] LongRetriever - Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
  
• 2025 (Bytedance) (KDD) [VQ] Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever
  
• 2025 (JD) (KDD) [UniERF] UniERF - A Uniform Embedding-based Retrieval Framework for E-Commerce Search
  
• 2025 (Meta) (KDD) [MTMH] Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
  
• 2025 (Meta) [RADAR ] RADAR - Recall Augmentation through Deferred Asynchronous Retrieval
  
• 2025 (Tencent) (Arxiv) An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction
  

ANN

• 2017 (Arxiv) (Meta) [FAISS] Billion-scale similarity search with GPUs
  
• 2020 (PAMI) [HNSW] Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
  
• 2021 (TPAMI) [IVF-PQ] Product Quantization for Nearest Neighbor Search
  

Graph_Neural_Networks

• 2017 (ICLR) [GCN] Semi-Supervised Classification with Graph Convolutional Networks
  
• 2018 (ICLR) [GAT] Graph Attention 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
  

LLM_Matching

• 2021 (Baidu) (KDD) Pre-trained Language Model for Web-scale Retrieval in Baidu Search
  
• 2023 (Google) (NIPS) [TIGER] Recommender Systems with Generative Retrieval
  
• 2024 (Alibaba) (WWW) [BEQUE] Large Language Model based Long-tail Query Rewriting in Taobao Search
  
• 2024 (Kuaishou) (Arxiv) [KuaiFormer] KuaiFormer - Transformer-Based Retrieval at Kuaishou
  
• 2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations
  
• 2024 (Meta) (Arxiv) Unifying Generative and Dense Retrieval for Sequential Recommendation
  
• 2024 (Xiaohongshu) (WWW) [NoteLLM] NoteLLM - Multimodal Large Representation Models for Recommendation
  
• 2025 (Kuaishou) (Arxiv)[OneRec] OneRec - Unifying Retrieve and Rank with Generative Recommender and Preference Alignment
  
• 2025 (Alibaba) (Arxiv) [TBGRecall] TBGRecall - A Generative Retrieval Model for E-commerce Recommendation Scenarios
  
• 2025 (Arxiv) (Tencent) [RARE] Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
  
• 2025 (Baidu) (Arxiv) [COBRA] Sparse Meets Dense -Unified Generative Recommendations with Cascaded Sparse-Dense Representations
  
• 2025 (Google) [PLUM] PLUM - Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
  
• 2025 (JD) (Arxiv) [GRAM] Generative Retrieval and Alignment Model - A New Paradigm for E-commerce Retrieval
  
• 2025 (Kuaishou) (AAAI) [Align3GR] Align3GR - Unified Multi-Level Alignment for LLM-based Generative Recommendation
  
• 2025 (Kuaishou) (Arxiv) [LARM] LLM-Alignment Live-Streaming Recommendationpdf
  
• 2025 (Kuaishou) (Arxiv) [LEARN] LEARN - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
  
• 2025 (Meta) (Arxiv) [DRAMA] DRAMA - Diverse Augmentation from Large Language Models to Smaller Dense Retrievers
  
• 2025 (Meta) (Arxiv) [ROO] Request-Only Optimization for Recommendation Systems
  
• 2025 (Pinterest) [PinRec] PinRec - Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems
  
• 2025 (Xiaohongshu) （KDD) [NoteLLM-2] NoteLLM-2 - Multimodal Large Representation Models for Recommendation
  

03_Pre-ranking

• 2020 (Alibaba) (DLP-KDD) [COLD] COLD - Towards the Next Generation of Pre-Ranking System
  
• 2022 (Huawei) (SIGIR) [RankFlow] RankFlow - JointOptimization ofMulti-Stage CascadeRanking SystemsasFlows
  
• 2022 （Huawei) (CIKM) [IntTower] IntTower - the Next Generation of Two-Tower Model for Pre-Ranking System
  
• 2023 (Alibaba) (CIKM) [COPR] COPR - Consistency-Oriented Pre-Ranking for Online Advertising
  
• 2023 (Alibaba) (KDD) [ASMOL] Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
  
• 2025 (Tencent) (Arxiv) [HIT] HIT Model - A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems
  

04_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) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction
  
• 2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
  
• 2019 (CIKM) ** [AutoInt] AutoInt -Automatic Feature Interaction Learning via Self-Attentive Neural Networks
  
• 2019 (Facebook) (Arxiv) [DLRM] (Facebook) Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook
  
• 2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system
  
• 2020 (Alibaba) (Arxiv) ** [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
  
• 2020 (Alibaba) (NIPS) Neuron-level Structured Pruning using Polarization Regularizer
  
• 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
  
• 2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
  
• 2021 (Alibaba) (CIKM) [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
  
• 2021 (Google) (WWW) * [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
  
• 2022 (Alibaba) (Arxiv) ** [ETA] Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
  
• 2022 (Alibaba) (WSDM) Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
  
• 2022 (Meta) ** (Arxiv) DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
  
• 2023 (Alibaba) (Arxiv) [ESLM] Entire Space Learning Framework - Unbias Conversion Rate Prediction in Full Stages of Recommender System
  
• 2023 (Google) (Arxiv) On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models
  
• 2023 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
  
• 2023 (Kuaishou) (Arixiv) [TWIN] TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
  
• 2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
  
• 2024 (Kuaishou) (CIKM) [TWINv2] TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
  
• 2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations
  
• 2024 (Meta) ** (PMLR) [Wukong] Wukong - Towards a Scaling Law for Large-Scale Recommendation
  
• 2025 (Bytedance) ** (Arxiv) [LONGER] LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders
  
• 2025 (Bytedance) ** (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders
  
• 2025 （Bytedance) ** (Arxiv) [STCA] Make It Long, Keep It Fast - End-to-End 10k-Sequence Modeling at Billion Scale on Douyin
  
• 2026 (Meta) (KDD) [Lattice] Meta Lattice - Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations
  

Activation-Function

• 2020(Arxiv) [GLU] GLU Variants Improve Transformer
  

Calibration

• 2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook
  
• 2014 (TIST) Simple and scalable response prediction for display advertising
  
• 2023 Classifier Calibration with ROC-Regularized Isotonic Regression
  

Classic

• 2016 (ICLR) [GRU4Rec] Session-based Recommendations with Recurrent Neural Networks
  
• 2017 (Amazon) (IEEE) Two decades of recommender systems at Amazon.com
  

DNN

• 2019 (KDD) (Airbnb) Applying Deep Learning To Airbnb Search
  
• 2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search
  

Delayed-Feedback-Problem

• 2008 (KDD) Learning Classifiers from Only Positive and Unlabeled Data
  
• 2014 (Criteo) (KDD) [DFM] Modeling Delayed Feedback in Display Advertising
  
• 2018 (Arxiv) [NoDeF] A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
  
• 2019 (Twitter) (RecSys) Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
  
• 2020 (AdKDD) Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions
  
• 2020 (JD) (IJCAI) [TS-DL] An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
  
• 2020 (SIGIR) [DLA-DF] Dual Learning Algorithm for Delayed Conversions
  
• 2020 (WWW) [FSIW] A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
  
• 2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
  
• 2021 (Alibaba) (AAAI) [ESDF] Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
  
• 2021 (Alibaba) (Arxiv) [Defer] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling
  
• 2021 (Google) (Arxiv) Handling many conversions per click in modeling delayed feedback
  
• 2021 (Tencent) (SIGIR) Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback
  
• 2022 (Alibaba) (WWW) [DEFUSE] Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
  

Distill

• 2020 (Alibaba) (KDD) *[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations
  
• 2024 (Google) Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders
  
• 2025 (Bytedance) (KDD) [HA-PFD] Hardness-aware Privileged Features Distillation with Latent Alignment for CVR Prediction
  
• 2025 (Kuaishou) [MIKD] Mutual Information-aware Knowledge Distillation for Short Video Recommendation
  

Experiment

• 2010 (Google) Overlapping Experiment Infrastructure - More, Better, Faster Experimentation
  
• 2019 (Alibaba) (KDD) OptimizedCost perClickin TaobaoDisplayAdvertising
  
• 2022 (Google) (KDD) Scale Calibration of Deep Ranking Models
  

Feature-Crossing

• 2010 (ICDM) [FM] Factorization machines
  
• 2013 (Google) (KDD) [LR] Ad Click Prediction - a View from the Trenches
  
• 2016 (Arxiv) [PNN] Product-based Neural Networks for User Response Prediction
  
• 2016 (Criteo) (Recsys) [FFM] Field-aware Factorization Machines for CTR Prediction
  
• 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 (Google) (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 (Google) (WSDM) [Latent Cross] Latent Cross Making Use of Context in Recurrent Recommender Systems
  
• 2018 (KDD) [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems
  
• 2018 (TOIS) [PNN] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
  
• 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 (Sina) (Arxiv) [FAT-DeepFFM] FAT-DeepFFM - Field Attentive Deep Field-aware Factorization Machine
  
• 2019 (Tencent) (AAAI) [IFM] Interaction-aware Factorization Machines for Recommender Systems
  
• 2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising
  
• 2021 (Google) (NIPS) [MLP-Mixer] MLP-Mixer - An all-MLP Architecture for Vision
  
• 2021 (Google) (WWW) * [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
  
• 2022 (Alibaba) (WSDM) * [CAN] CAN - Feature Co-Action Network for Click-Through Rate Prediction
  
• 2022 (Meta) ** (Arxiv) DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
  
• [2023 (CIKM) * [GDCN]

README 内容过长，已截取部分内容。完整内容请前往 GitHub 仓库查看。