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 (Alibaba) (KDD) [MIMN] Practice on Long Sequential User Behavior Modeling 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
  
• 2019 (Google) (WWW) Towards Neural Mixture Recommender for Long Range Dependent User Sequences
  
• 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 (ICLR) Reformer - The Efficient Transformer
  
• 2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  
• 2020 (SIGIR) [UBR4CTR] User Behavior Retrieval for Click-Through Rate Prediction
  
• 2020 (Tencent) (Recsys) ** [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
  
• 2021 (Alibaba) (Arxiv) [ETA] End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model
  
• 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 (Meituan) (CIKM) [SDIM] Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
  
• 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) (CIKM) [QIN] Query-dominant User Interest Network for Large-Scale Search Ranking
  
• 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
  
• 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
  
• 2025 (Alibaba) (Arxiv) [MUSE] MUSE - A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
  
• 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 (Kuaishou) (KDD) [HiT-LBM] Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model
  
• 2025 (Meta) (Arxiv) [VISTA] Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
  
• 2025 (Meta) (KDD) DV365 - Extremely Long User History Modeling at Instagram
  
• 2025 (Pinterest) (Arxiv) [TransActV2]TransAct V2 - Lifelong User Action Sequence Modeling on Pinterest Recommendation
  
• 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
  
• 2026 （Bytedance) (Arxiv) [Rec-Distill] Rec-Distill - An Industrial Distillation Pipeline for Large-Scale Recommendation Models
  

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] Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
  
• 2023 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
  
• 2023 (Sina) (CIKM) [MemoNet] MemoNet - Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
  
• 2024 (Google) (ICLR) From Sparse to Soft Mixtures of Experts
  
• 2024 (Meta) ** (PMLR) [Wukong] Wukong - Towards a Scaling Law for Large-Scale Recommendation
  
• 2024 （LinkedIn) (KDD) [RDCN] LiRank - Industrial Large Scale Ranking Models at LinkedIn
  
• 2025 (Alibaba) [HHFT] HHFT - Hierarchical Heterogeneous Feature Transformer for Recommendation Systems
  
• 2025 (Bytedance) (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders
  
• 2025 (Bytedance) ** (Arxiv) [Pyramid Mixer] Pyramid Mixer - Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation
  
• 2025 (Bytedance) ** (Arxiv) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders
  
• 2025 (Bytedance) ** (CIKM) [RankMixer] RankMixer - Scaling Up Ranking Models in Industrial Recommenders
  
• 2025 (Google) ** (Arxiv) [Hiformer] Hiformer - Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
  
• 2025 (Meta) (CIKM) [InterFormer] InterFormer - Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
  
• 2025 (Tencent) (Arxiv) [D-MoE] Enhancing CTR Prediction with De-correlated Expert Networks
  
• 2025 （Alibaba) [FAT] From Scaling to Structured Expressivity - Rethinking Transformers for CTR Prediction
  
• 2025 （Bytedance) (Arxiv) [OneTrans] OneTrans - Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender
  
• 2026 (Alibaba) (Arxiv) [EST] EST - Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
  
• 2026 (Alibaba) (Arxiv) [MGDIN] Deferred is Better - A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
  
• 2026 (Bytedance) (Arxiv) [MixFormer] MixFormer - Co-Scaling Up Dense and Sequence in Industrial Recommenders
  
• 2026 (Bytedance) (Arxiv) [TokenMixer-Large] TokenMixer-Large - Scaling Up Large Ranking Models in Industrial Recommenders
  
• 2026 (Bytedance) (Arxiv) [UG-Sep] Compute Only Once - UG-Separation for Efficient Large Recommendation Models
  
• 2026 (Bytedance) (Arxiv) [Zenith] Zenith - Scaling up Ranking Models for Billion-scale Livestreaming Recommendation
  
• 2026 (Coupon) [Arxiv] On the Practice of Scaling Search Conversion Rate Prediction
  
• 2026 (Kuaishou) (Arxiv) [UniMixer] UniMixer - A Unified Architecture for Scaling Laws in Recommendation Systems
  
• 2026 (Tencent) (Arxiv) [RankUp] RankUp - Towards High-rank Representations for Large Scale Advertising Recommender Systems
  
• 2026 （Bytedance) (Arxiv) [Rec-Distill] Rec-Distill - An Industrial Distillation Pipeline for Large-Scale Recommendation Models
  
• 2026 （Tencent) (Arxiv) [TokenFormer] TokenFormer - Unify the Multi-Field and Sequential Recommendation Worlds
  

Feature_Importance

• 2022 (Kuaishou) (Arxiv) [LPFS] LPFS - Learnable Polarizing Feature Selection for Click-Through Rate Prediction
  
• 2024 (Huawei)(KDD) ERASE - Benchmarking Feature Selection Methods for Deep Recommender Systems
  

Gating

• 2014 (TASLP) * [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
  
• 2018 (CVPR) * [SENet] Squeeze-and-Excitation Networks
  
• 2019 (Sina) (Recsys) [FiBiNET] FiBiNET - combining feature importance and bilinear feature interaction for click-through rate prediction
  
• 2020 (Sina) (Arxiv) [GateNet] GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction
  
• 2021 (Sina) (Arxiv) [ContextNet] ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
  
• 2021 (Sina) （DLP-KDD) [MaskNet] MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
  
• 2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
  
• 2023 (Sina) (CIKM) [FiBiNet++] FiBiNet++ - Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
  
• 2025 (Bytedance) [ADS] Adaptive Domain Scaling for Personalized Sequential Modeling in Recommenders
  

LLM_Ranking

• 2019 (CIKM) [AutoInt] AutoInt -Automatic Feature Interaction Learning via Self-Attentive Neural Networks
  
• 2020 (Arxiv) Scaling Laws for Neural Language Models
  
• 2021 (Baidu) (KDD) Pre-trained Language Model based Ranking in Baidu Search
  
• 2023 (Arxiv) [E4SRec] E4SRec - An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation
  
• 2024 (Alibaba) (Arxiv) [BAHE] Breaking the Length Barrier - LLM-Enhanced CTR Prediction in Long Textual User Behaviors
  
• 2024 (Bytedance) (Arxiv) [HLLM] HLLM - Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling
  
• 2024 (Google) (Arxiv) LLMs for User Interest Exploration in Large-scale Recommendation Systems
  
• 2024 (Google) (Arxiv) [CALRec] CALRec - Contrastive Alignment of Generative LLMs for Sequential Recommendation
  
• 2024 (Kuaishou) (Arxiv) [LEARN] LEARN - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
  
• 2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation
  
• 2024 (Meta) (Arxiv) Unifying Generative and Dense Retrieval for Sequential Recommendation
  
• 2024 (Meta) (Arxiv) [SUM] Scaling User Modeling - Large-scale Online User Representations for Ads Personalization in Meta
  
• 2025 (Alibaba) (Arxiv) Unlocking Scaling Law in Industrial Recommendation Systems with a Three-step Paradigm based Large User Model
  
• 2025 (Alibaba) (Arxiv) [HeterRec] Hierarchical Causal Transformer with Heterogeneous Information for Expandable Sequential Recommendation
  
• 2025 (Alibaba) (Arxiv) [URM] Large Language Models Are Universal Recommendation Learners
  
• 2025 (Alibaba) (KDD) [GPSD] Scaling Transformers for Discriminative Recommendation via Generative Pretraining
  
• 2025 (Alibaba) (WWW) Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
  
• 2025 (Amazon) (Arxiv) SynerGen - Contextualized Generative Recommender for Unified Search and Recommendation
  
• 2025 (Arxiv) (Pinterest) [PinRec] PinRec - Outcome-Conditioned, Multi-Token Generative Retrieval for Industry-Scale Recommendation Systems
  
• 2025 (Arxiv) (Xiaohongshu) [GenRank] Towards Large-scale Generative Ranking
  
• 2025 (Baidu) (Arxiv) [COBRA] Sparse Meets Dense -Unified Generative Recommendations with Cascaded Sparse-Dense Representations
  
• 2025 (Google) (Arxiv) User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
  
• 2025 (Google) (Arxiv) [STAR] STAR - A Simple Training-free Approach for Recommendations using Large Language Models
  
• 2025 (Kuaishou) (Arxiv) [GenSAR] Unified Generative Search and 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 (Kuaishou) (Arxiv) [OneRec-V2] OneRec Technical Report v2
  
• 2025 (Kuaishou) (Arxiv) [OneRec] OneRec - Unifying Retrieve and Rank with Generative Recommender and Preference Alignment
  
• 2025 (Kuaishou) (Arxiv) [OneRec] OneRec Technical Report
  
• 2025 (Meituan) (Arxiv) [DFGR] Action is All You Need - Dual-Flow Generative Ranking Network for Recommendation
  
• 2025 (Meituan) (Arxiv) [MTGR] MTGR - Industrial-Scale Generative Recommendation Framework in Meituan
  
• 2025 (Meituan) (Arxiv) [UniROM] One Model to Rank Them All - Unifying Online Advertising with End-to-End Learning
  
• 2025 (Meta) (Arxiv) [HyperCast] Realizing Scaling Laws in Recommender Systems - A Foundation–Expert Paradigm for Hyperscale Model Deployment
  
• 2025 (Microsoft) (KDD)Towards Web-scale Recommendations with LLMs - From Quality-aware Ranking to Candidate Generation
  
• 2025 (Pinterest) (Arxiv) [PinFM] PinFM - Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
  
• 2025 (Tencent) (Arxiv) [GPR] GPR - Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation
  
• 2025 (eBay) (Arxiv) LLMDistill4Ads - Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay
  
• 2025 （Bytedance) (Arxiv) [OneTrans] OneTrans - Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender
  
• 2025 （Meta) (Arxiv) [ExFM] External Large Foundation Model - How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
  
• 2026 (Alibaba) (Arxiv) [SORT] SORT - A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders
  
• 2026 (Bytedance) (Arxiv) [HyFormer] HyFormer - Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction
  
• 2026 (Bytedance) (Arxiv) [MixFormer] MixFormer - Co-Scaling Up Dense and Sequence in Industrial Recommenders
  
• 2026 (Kuaishou) (Arxiv) [GR4AD] Generative Recommendation for Large-Scale Advertising
  
• 2026 (Meta) (Arxiv) [Kunlun] Kunlun - Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
  
• 2026 (Meta) (Arxiv) [WHALE] WHALE - A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture
  
• 2026 （Bytedance) (Arxiv) [MDL] MDL - A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization
  
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Loss

• 2015 (Twitter) (KDD) Click-through Prediction for Advertising in Twitter Timeline
  
• 2022 (Google) (KDD) Scale Calibration of Deep Ranking Models
  
• 2023 (Alibaba) (KDD) Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
  
• 2023 (Google) (CIKM) Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
  
• 2024 (Tencent) (KDD) Understanding the Ranking Loss for Recommendation with Sparse User Feedback
  

Multi-Modal

• 2018 (Alibaba) (CIKM) [Image CTR] Image Matters - Visually Modeling User Behaviors Using Advanced Model Server
  
• 2024 (Alibaba) (CIKM) Enhancing Taobao Display Advertising with Multimodal Representations - Challenges, Approaches and Insights
  
• 2026 (Alibaba) (WSDM) [MOON] MOON - Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding
  

Multi-domain-Multi-Scenario

• 2014 (TASLP) * [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
  
• 2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
  
• 2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
  
• 2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search
  
• 2020 (Alibaba) (Arxiv) [SAML] Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
  
• 2020 (Alibaba) (CIKM) [HMoE] Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space
  
• 2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
  
• 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) (ICLR) HyperGrid Transformers - Towards A Single Model for Multiple Tasks
  
• 2021 (Kwai) (Arxiv) [POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems
  
• 2022 (Alibaba) (CIKM) AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
  
• 2022 (Alibaba) (NIPS) ** [APG] APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction
  
• 2023 (Alibaba) (CIKM) [HC2] Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
  
• 2023 (Alibaba) (CIKM) [MMN] Masked Multi-Domain Network - Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model
  
• 2023 (Alibaba) (CIKM) [Rec4Ad] Rec4Ad - A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
  
• 2023 (Alibaba) (SIGIR) [MARIA] Multi-Scenario Ranking with Adaptive Feature Learning
  
• 2023 (CIKM) [HAMUR] HAMUR - Hyper Adapter for Multi-Domain Recommendation
  
• 2023 (Huawei) (CIKM) [DFFM] DFFM - Domain Facilitated Feature Modeling for CTR Prediction
  
• 2023 (Kuaishou) (KDD) * [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
  
• 2023 (Tencent) (KDD) Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction
  
• 2024 (Alibaba) (CIKM) * [MultiLoRA] MultiLoRA - Multi-Directional Low-Rank Adaptation for Multi-Domain Recommendation
  
• 2024 (Alibaba) (RecSys) * [MLoRA] MLoRA - Multi-Domain Low-Rank Adaptive Network for Click-Through Rate Prediction
  
• 2024 (Kuaishou) (SIGIR) [M3oE] M3oE - Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework
  
• 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
  
• 2024 (WSDM) Exploring Adapter-based Transfer Learning for Recommender Systems - Empirical Studies and Practical Insights
  
• 2025 (Kuaishou) (KDD) [HoME] HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou
  

Multi-task

• (2018) (ICML) GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
  
• 2014 (TASLP) [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
  
• 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 (CVPR) Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
  
• 2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
  
• 2019 (Alibaba) (CIKM) Multi-task based Sales Predictions for Online Promotions
  
• 2019 (Alibaba) (Recys) A Pareto-Eficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation
  
• 2019 (Google) (AAAI) SNR Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
  
• 2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system
  
• 2019 (NIPS) Pareto Multi-Task Learning
  
• 2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
  
• 2020 (Alibaba) (WWW) Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
  
• 2020 (Amazon) (WWW) Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation
  
• 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
  
• 2021 (Alibaba) (SIGIR) [HM3] Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
  
• 2021 (Alibaba) (SIGIR) [MSSM] MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning
  
• 2021 (Baidu) (SIGIR) [GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction
  
• 2021 (Google) (Arxiv) [DSelect-k] DSelect-k Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
  
• 2021 (Google) (KDD) Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
  
• 2021 (JD) (ICDE) Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
  
• 2021 (Meituan) (KDD) Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
  
• 2021 (Tencent) (Arxiv) Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
  
• 2021 (Tencent) (WWW) Personalized Approximate Pareto-Efficient Recommendation
  
• 2022 (Google) (WWW) Can Small Heads Help? Understanding and Improving Multi-Task Generalization
  
• 2023 (Airbnb) (KDD) Optimizing Airbnb Search Journey with Multi-task Learning
  
• 2023 (Alibaba) (CIKM) [DTRN] Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
  
• 2023 (Google) (CIKM) Multitask Ranking System for Immersive Feed and No More Clicks - A Case Study of Short-Form Video Recommendation
  
• 2023 (Google) (KDD) Improving Training Stability for Multitask Ranking Models in Recommender Systems
  
• 2023 (Meta) (KDD) AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
  
• 2024 (Airbnb) (KDD) Multi-objective Learning to Rank by Model Distillation
  
• 2024 (Kuaishou) (KDD) [GradCraft] GradCraft - Elevating Multi-task Recommendations through Holistic Gradient Crafting
  
• 2024 (Kuaishou) [HoME] HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou
  
• 2024 (Shopee) (KDD) [ResFlow] Residual Multi-Task Learner for Applied Ranking
  
• 2024 (Tencent) (KDD) [STEM] Ads Recommendation in a Collapsed and Entangled World
  
• 2025 (Baidu) (KDD) [RankExpert] RankExpert - A Mixture of Textual-and-Behavioral Experts for Multi-Objective Learning-to-Rank in Web Search
  
• 2025 (Byetedance) (CIKM) [PMTA] PMTA - Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation
  
• 2025 （Alibaba) (CIKM) [MAL] See Beyond a Single View - Multi-Attribution Learning Leads to Better Conversion Rate Prediction
  
• 2026 (Bytedance) (Arxiv) [MDL] MDL - A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization
  

ParameterServer

• 2014 (Baidu) (OSDI) Scaling Distributed Machine Learning with the Parameter Server
  
• 2019 (Alibaba) (DLP-KDD) [XDL] XDL - An Industrial Deep Learning Framework for High-dimensional Sparse Data
  
• 2020 (Bytedance) (OSDI) [BytePS] A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU:CPU Clusters
  
• 2022 (Kuaishou) (KDD) [Persia] Persia - An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
  

Pre-training

• 2019 (Alibaba) (IJCAI) [DeepMCP] Representation Learning-Assisted Click-Through Rate Prediction
  
• 2019 (SIGIR) [BERT4Rec] (Alibaba) (SIGIR2019) BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer
  

Sequence-Modeling

• 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
  
• 2017 (Google) (NIPS) ** Attention Is All You Need
  
• 2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction
  
• 2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
  
• 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) (KDD) [BST] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
  
• 2019 (Tencent) (KDD) [RALM] TReal-time Attention Based Look-alike Model for Recommender System
  
• 2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
  
• 2020 (JD) (NIPS) [KFAtt] Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
  
• 2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems
  
• 2022 (Alibaba) (WSDM) [RACP] Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
  
• 2022 (JD) (WWW) Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
  
• 2022 (WWW) [FMLP] Filter-enhanced MLP is All You Need for Sequential Recommendation
  
• 2023 (Meituan) (CIKM) [DCIN] Deep Context Interest Network for Click-Through Rate Prediction
  
• 2023 (Pinterest) (KDD) TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest
  
• 2024 (Meta) (Arxiv) ** [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations
  
• 2025 (Alibaba) (Arxiv) [LREA] Efficient Long Sequential Low-rank Adaptive Attention for Click-through rate Prediction
  
• 2025 (Bytedance) ** (Arxiv) [LONGER] LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders
  
• 2025 (Kuaishou) (SIGIR) [FIM] FIM - Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation
  
• 2025 （Bytedance) ** (Arxiv) [STCA] Make It Long, Keep It Fast - End-to-End 10k-Sequence Modeling at Billion Scale on Douyin
  
• 2026 (Bytedance) (Arxiv) [TM20K] Teacher Retains Full Tokens, Student Merges Efficiently - TM20K for E-Commerce Sequence Modeling in Ad Recommendation
  
• 2026 (Meta) (Arxiv) [LLaTTE] LLaTTE - Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation
  
• 2026 (Meta) (Arxiv) [ULTRA-HSTU] Bending the Scaling Law Curve in Large-Scale Recommendation Systems
  
• 2026 (Meta) Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
  
• 2026 （Bytedance) (Arxiv) [SequenceO1] SequenceO1 - End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching
  

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
  
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Trigger

• 2022 (Alibaba) (WWW) Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
  

05_Post-ranking

• 1998 (SIGIR) ** [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
  
• 2005 (WWW) Improving Recommendation Lists Through Topic Diversification
  
• 2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation
  
• 2009 (Microsoft) (WSDM) Diversifying Search Results
  
• 2010 (WWW) Exploiting Query Reformulations for Web Search Result Diversification
  
• 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) (Arxiv) Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology
  
• 2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns
  
• 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
  
• 2020 (Huawei) (Arxiv) Personalized Re-ranking for Improving Diversity in Live Recommender Systems
  
• 2021 (Alibaba) (Arxiv) ** [PRS] Revisit Recommender System in the Permutation Prospective
  
• 2021 (Google) (WSDM) User Response Models to Improve a REINFORCE Recommender System
  
• 2021 (Microsoft) Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective
  
• 2023 (Amazon) (KDD) RankFormer - Listwise Learning-to-Rank Using Listwide Labels
  
• 2023 (Meituan) (KDD) PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
  
• 2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation
  
• 2025 (Alibaba) (SIGIR) [SORT-Gen] A Generative Re-ranking Model for List-level Multi-objective Optimization at 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
  

06_Relevance-ranking

• 2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data
  
• 2016 (Yahoo) (KDD) Ranking Relevance in Yahoo Search
  
• 2020 (ICLR) [StructBERT] StructBERT - Incorporating Language Structures into Pre-training for Deep Language Understanding
  
• 2021 (Alibaba) (WWW) [MASM] Learning a Product Relevance Model from Click-Through Data in E-Commerce
  
• 2022 (Alibaba) (KDD) [ReprBERT] ReprBERT - Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce
  
• 2023 (Meituan) (CIKM) [SPM] SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
  
• 2024 (Alibaba) (KDD) [DeepBoW] Deep Bag-of-Words Model - An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce
  
• 2024 （Walmart) (SIGIR) Large Language Models for Relevance Judgment in Product Search
  
• 2025 (Alibaba) (Arxiv) LORE - A Large Generative Model for Search Relevance
  
• 2025 (Alibaba) (WWW) [ELLM] Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
  
• 2025 (Kuaishou) (Arxiv) [HCMRM] HCMRM -A High-Consistency Multimodal Relevance Model for Search Ads
  
• 2025 (Linkedin) (Arxiv) Powering Job Search at Scale - LLM-Enhanced Query Understanding in Job Matching Systems
  
• 2025 （Alibaba) (Arxiv) TaoSR1 - The Thinking Model for E-commerce Relevance Search
  
• 2025 （Tencent) (KDD) [GenFR] Applying Large Language Model For Relevance Search In Tencent
  

07_LLM

01_LLM_Classical

• 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
  
• 2014 (Google) (NIPS) [Seq2Seq] Sequence to Sequence Learning with Neural Networks
  
• 2017 (Google) (NIPS) [Transformer] Attention Is All You Need
  
• 2017 (OpenAI) (NIPS) [RLHF] Deep Reinforcement Learning from Human Preferences
  
• 2018 (OpenAI) (Arxiv) [GPT-1] Improving Language Understanding by Generative Pre-Training
  
• 2019 (Google) (NAACL) [Bert] BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding
  
• 2019 (OpenAI) (Arxiv) [GPT-2] Language Models are Unsupervised Multitask Learners
  
• 2020 (Arxiv) Scaling Laws for Neural Language Models
  
• 2020 (Meta) (NIPS) [RAG] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
  
• 2020 (OpenAI) (Arxiv) [GPT-3] Language Models are Few-Shot Learners
  
• 2021 (Microsoft) (Arxiv) [LoRA] LoRA - Low-Rank Adaptation of Large Language Models
  
• 2022 (Google) (Arxiv) [PaLM] PaLM - Scaling Language Modeling with Pathways
  
• 2022 (Google) (JMLR) [SwitchTransfomers] Switch Transformers - Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
  
• 2022 (Google) (NIPS) [COT] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  
• 2022 (Google) (NIPS) [ChainOfThought] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  
• 2022 (Google) (TMLR) [Emergent] Emergent Abilities of Large Language Models
  
• 2022 (OpenAI) (Arxiv) [InstructGPT] [RLHF] Training language models to follow instructions with human feedback
  
• 2022 (OpenAI) (Arxiv) [PPO] Proximal Policy Optimization Algorithms
  
• 2022 (OpenAI) (Arxiv) [WebGPT] Learning to summarize from human feedback
  
• 2022 (OpenAI) (Arxiv) [WebGPT] WebGPT - Browser-assisted question-answering with human feedback
  
• 2023 (Alibaba) (Arxiv) [QWEN] QWEN Technical Report
  
• 2023 (Meta) (Arxiv) [LLaMA-2] Llama 2 - Open Foundation and Fine-Tuned ChatModels
  
• 2023 (Meta) (Arxiv) [LLaMA] LLaMA - Open and Efficient Foundation Language Models
  
• 2023 (OpenAI) (Arxiv) [GPT4] GPT-4 Technical Report
  
• 2024 (Alibaba) (Arxiv) [QWEN2] QWEN2 Technical Report
  
• 2024 (Arxiv) [TinyLlama] Arxiv TinyLlama - An Open-Source Small Language Model
  
• 2024 (DeepSeek) (Arxiv) [GRPO] DeepSeekMath - Pushing the Limits of Mathematical Reasoning in Open Language Models
  
• 2025 (Alibaba) (Arxiv) [QWEN-2.5] QWEN 2.5 Technical Report
  
• 2025 (Alibaba) (Arxiv) [Qwen2.5-VL] Qwen2.5-VL Technical Report
  
• 2025 (Alibaba) (Arxiv) [Qwen3 Embedding] Qwen3 Embedding - Advancing Text Embedding and Reranking Through Foundation Models
  
• 2025 (Alibaba) (Arxiv) [Qwen3] Qwen3 Technical Report
  
• 2025 (Arxiv) A Survey of Large Language Models
  
• 2025 (DeepSeek) (Nature) [DeepSeek-R1] DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
  
• 2025 (DeepSeek) [DeepSeek-R1] DeepSeek-R1 -Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
  
• 2025 (DeepSeek) [DeepSeek-V3] DeepSeek-V3 Technical Report
  
• 2025 (Google) (Arxiv) [SigLIP2] SigLIP 2 - Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
  
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• resources
  

02_Self_Supervised_Learning

• 2020 (Alibaba) (AAAI) [DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction
  
• 2020 (Alibaba) (CIKM) [BERT4Rec] BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer
  
• 2020 (Alibaba) (KDD) Disentangled Self-Supervision in Sequential Recommenders
  
• 2020 (Arxiv) UserBERT - Self-supervised User Representation Learning
  
• 2020 (Arxiv) [SGL] Self-supervised Graph Learning for Recommendation
  
• 2020 (CIKM) [S3Rec] S3-Rec - Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
  
• 2020 (EMNLP) [PTUM] PTUM - Pre-training User Model from Unlabeled User Behaviors via Self-supervision
  
• 2020 (SIGIR) Self-Supervised Reinforcement Learning for Recommender Systems
  
• 2021 (Alibaba) (Arxiv) [CLRec] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
  
• 2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
  
• 2021 (Alibaba) (WWW) Contrastive Pre-training for Sequential Recommendation
  
• 2021 (Google) (CIKM) Self-supervised Learning for Large-scale Item Recommendations
  
• 2021 (WSDM) [Prop] PROP - Pre-training with Representative Words Prediction for Ad-hoc Retrieval
  

08_Deep_Learning

• 2012 (NIPS) [CNN] ImageNet Classification with Deep Convolutional Neural Networks
  
• 2014 (JMLR) [Dropout] Dropout - A Simple Way to Prevent Neural Networks from Overfitting
  
• 2015 (Google) (JMLR) [BatchNorm] Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift
  
• 2015 (OpenAI) (ICLR) [Adam] Adam - A Method for Stochastic Optimization
  
• 2016 (CVPR) [ResNet] Deep Residual Learning for Image Recognition
  
• 2016 (OpenAI) (NIPS) [Weight Norm] Weight Normalization - A Simple Reparameterization to Accelerate Training of Deep Neural Networks
  
• 2017 (Arxiv) [LayerNorm] Layer Normalization
  
• 2017 (Google) (NIPS) [Transformer] Attention Is All You Need
  
• 2020 (Arxiv) GLU Variants Improve Transformer
  
• 2020 (ICML) On Layer Normalization in the Transformer Architecture
  

09_Reinforcement_Learning

• 2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article 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 (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 (Sigweb) Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey
  
• 2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise
  
• 2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering
  

RL_classical

• 1992 (ML) [REINFORCE] Simple statistical gradient-following algorithms for connectionist reinforcement learning
  
• 1999 (NIPS) [Actor-Critic] Actor-Critic Algorithms
  
• 2013 (DeepMind) (Arxiv) [DQN] Playing Atari with Deep Reinforcement Learning
  
• 2015 (DeepMind) (AAAI) [Double Q-learning] Deep Reinforcement Learning with Double Q-learning
  
• 2015 (DeepMind) (Nature) [DQN] Human-level control through deep reinforcement learning
  
• 2016 (Google) (Arxiv) [A3C] Asynchronous Methods for Deep Reinforcement Learning
  
• 2016 (OpenAI) (Nature) Mastering the game of Go with deep neural networks and tree search
  
• 2017 (JMLR) [TRPO] Trust Region Policy Optimization
  
• 2017 (OpenAI) (Arxiv) [PPO] Proximal Policy Optimization Algorithms
  
• 2017 （OpenAI) (Arxiv) [ES] Evolution Strategies as a Scalable Alternative to Reinforcement Learning