Awesome
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommendation, LLM), Transfer learning, Reinforcement Learning and so on.
00_Embedding
- 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality <br />
- 2014 (KDD) [DeepWalk] DeepWalk - online learning of social representations <br />
- 2015 (WWW) [LINE] LINE Large-scale Information Network Embedding <br />
- 2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks <br />
- 2017 (ICLR) [GCN] Semi-supervised Classification with Graph Convolutional Networks <br />
- 2017 (KDD) [Struc2vec] struc2vec - Learning Node Representations from Structural Identity <br />
- 2017 (NIPS) [GraphSAGE] Inductive Representation Learning on Large Graphs <br />
- 2018 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb <br />
- 2018 (Alibaba) (KDD) *[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba <br />
- 2018 (ICLR) [GAT] Graph Attention Networks <br />
- 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems <br />
- 2018 (WSDM) [NetMF] Network embedding as matrix factorization - Unifying deepwalk, line, pte, and node2vec <br />
- 2019 (Alibaba) (KDD) *[GATNE] Representation Learning for Attributed Multiplex Heterogeneous Network <br />
01_Matching
- 2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data <br />
- 2015 (KDD) [Sceptre] Inferring Networks of Substitutable and Complementary Products <br />
- 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations <br />
- 2018 (Alibaba) (KDD) * [TDM] Learning Tree-based Deep Model for Recommender Systems <br />
- 2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems <br />
- 2019 (Alibaba) (CIKM) **[MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall <br />
- 2019 (Alibaba) (CIKM) *[SDM] SDM - Sequential deep matching model for online large-scale recommender system <br />
- 2019 (Alibaba) (NIPS) *[JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems <br />
- 2019 (Amazon) (KDD) Semantic Product Search <br />
- 2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search <br />
- 2019 (Google) (RecSys) **[Two-Tower] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations <br />
- 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System <br />
- 2019 [Tencent] (KDD) A User-Centered Concept Mining System for Query and Document Understanding at Tencent <br />
- 2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search <br />
- 2020 (Alibaba) (KDD) *[ComiRec] Controllable Multi-Interest Framework for Recommendation <br />
- 2020 (Facebook) (KDD) **[Embedding for Facebook Search] Embedding-based Retrieval in Facebook Search <br />
- 2020 (Google) (WWW) *[MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations <br />
- 2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items <br />
- 2020 (JD) (SIGIR) [DPSR] Towards Personalized and Semantic Retrieval - An End-to-EndSolution for E-commerce Search via Embedding Learning <br />
- 2020 (Microsoft) (Arxiv) TwinBERT - Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval <br />
- 2021 (Alibaba) (KDD) * [MGDSPR] Embedding-based Product Retrieval in Taobao Search <br />
- 2021 (Amazon) (KDD) Extreme Multi-label Learning for Semantic Matching in Product Search <br />
- 2021 (Bytedance) (Arxiv) [DR] Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations <br />
- 2022 (Alibaba) (CIKM) **[NANN] [二向箔] Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation <br />
- 2022 (Alibaba) (CIKM) [CLE-QR] Query Rewriting in TaoBao Search <br />
- 2022 (Alibaba) **(CIKM) [MOPPR] Multi-Objective Personalized Product Retrieval in Taobao Search <br />
- 2024 (Bytedance) (KDD) [Trinity] Trinity - Syncretizing Multi-:Long-Tail:Long-Term Interests All in One <br />
ANN
- 2017 (Arxiv) (Meta) [FAISS] Billion-scale similarity search with GPUs <br />
- 2020 (PAMI) [HNSW] Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs <br />
- 2021 (TPAMI) [IVF-PQ] Product Quantization for Nearest Neighbor Search <br />
Graph_Neural_Networks
- 2017 (ICLR) [GCN] Semi-Supervised Classification with Graph Convolutional Networks <br />
- 2018 (ICLR) [GAT] Graph Attention Networks <br />
- 2018 (Pinterest) (KDD) [PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems <br />
- 2019 (Alibaba) (KDD) [IntentGC] IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation <br />
- 2019 (Alibaba) (KDD) [MEIRec] Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation <br />
- 2019 (Alibaba) (SIGIR) [GIN] Graph Intention Network for Click-through Rate Prediction in Sponsored Search <br />
- 2020 (Alibaba) (SIGIR) [ATBRG] ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation <br />
02_Pre-ranking
- 2020 (Alibaba) (DLP-KDD) [COLD] COLD - Towards the Next Generation of Pre-Ranking System <br />
- 2023 (Alibaba) (CIKM) [COPR] COPR - Consistency-Oriented Pre-Ranking for Online Advertising <br />
- 2023 (Alibaba) (KDD) [ASMOL] Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System <br />
03_Ranking
- 2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook <br />
- 2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems <br />
- 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations <br />
- 2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction <br />
- 2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction <br />
- 2019 (Facebook) (Arxiv) [DLRM] (Facebook) Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook <br />
- 2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system <br />
- 2020 (Alibaba) (Arxiv) ** [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction <br />
- 2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems <br />
- 2020 (Tencent) (Recsys) ** [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations <br />
- 2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce <br />
- 2021 (Alibaba) (CIKM) [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction <br />
- 2022 (Alibaba) (Arxiv) ** [ETA] Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction <br />
- 2022 (Alibaba) (WSDM) Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search <br />
- 2022 (Meta) [DHEN] DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction <br />
- 2022 (WWW) [FMLP] Filter-enhanced MLP is All You Need for Sequential Recommendation <br />
- 2023 (Alibaba) (Arxiv) [ESLM] Entire Space Learning Framework - Unbias Conversion Rate Prediction in Full Stages of Recommender System <br />
- 2023 (Google) (Arxiv) On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models <br />
- 2023 (Kuaishou) (Arixiv) [TWIN] TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou <br />
- 2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information <br />
- 2024 (Kuaishou) (CIKM) [TWINv2] TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou <br />
- 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction <br />
Classic
- 2003 (Amazon) (IEEE) [CF] Amazon.com recommendations - Item-to-item collaborative filtering <br />
- 2009 (Computer) [MF] Matrix factorization techniques for recommender systems <br />
- 2016 (ICLR) [GRU4Rec] Session-based Recommendations with Recurrent Neural Networks <br />
- 2017 (Amazon) (IEEE) Two decades of recommender systems at Amazon.com <br />
DNN
- 2019 (KDD) (Airbnb) Applying Deep Learning To Airbnb Search <br />
- 2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search <br />
Delayed-Feedback-Problem
- 2008 (KDD) Learning Classifiers from Only Positive and Unlabeled Data <br />
- 2014 (Criteo) (KDD) [DFM] Modeling Delayed Feedback in Display Advertising <br />
- 2018 (Arxiv) [NoDeF] A Nonparametric Delayed Feedback Model for Conversion Rate Prediction <br />
- 2019 (Twitter) (RecSys) Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction <br />
- 2020 (AdKDD) Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions <br />
- 2020 (JD) (IJCAI) [TS-DL] An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration <br />
- 2020 (SIGIR) [DLA-DF] Dual Learning Algorithm for Delayed Conversions <br />
- 2020 (WWW) [FSIW] A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback <br />
- 2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling <br />
- 2021 (Alibaba) (AAAI) [ESDF] Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction <br />
- 2021 (Alibaba) (Arxiv) [Defer] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling <br />
- 2021 (Google) (Arxiv) Handling many conversions per click in modeling delayed feedback <br />
- 2021 (Tencent) (SIGIR) Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback <br />
- 2022 (Alibaba) (WWW) [DEFUSE] Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction <br />
Feature-Crossing
- 2010 (ICDM) [FM] Factorization machines <br />
- 2013 (Google) (KDD) [LR] Ad Click Prediction - a View from the Trenches <br />
- 2016 (Arxiv) [PNN] Product-based Neural Networks for User Response Prediction <br />
- 2016 (Criteo) (Recsys) [FFM] Field-aware Factorization Machines for CTR Prediction <br />
- 2016 (ECIR) [FNN] Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction <br />
- 2016 (KDD) [Deepintent] Deepintent - Learning attentions for online advertising with recurrent neural networks <br />
- 2016 (Microsoft) (KDD) [Deep Crossing] Deep Crossing - Web-scale modeling without manually crafted combinatorial features <br />
- 2017 (Google) (ADKDD) [DCN] Deep & CrossNetwork for Ad Click Predictions <br />
- 2017 (Huawei) (IJCAI) [DeepFM] DeepFM - A Factorization-Machine based Neural Network for CTR Prediction <br />
- 2017 (IJCAI) [AFM] Attentional Factorization Machines Learning the Weight of Feature Interactions via Attention Networks <br />
- 2017 (SIGIR) [NFM] Neural Factorization Machines for Sparse Predictive Analytics <br />
- 2017 (WWW) [NCF] Neural Collaborative Filtering <br />
- 2018 (CVPR) * [SENet] Squeeze-and-Excitation Networks <br />
- 2018 (Google) (WSDM) [Latent Cross] Latent Cross Making Use of Context in Recurrent Recommender Systems <br />
- 2018 (KDD) [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems <br />
- 2018 (TOIS) [PNN] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data <br />
- 2019 (CIKM) ** [AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks <br />
- 2019 (Huawei) (WWW) [FGCNN] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction <br />
- 2019 (Sina) (Arxiv) [FAT-DeepFFM] FAT-DeepFFM - Field Attentive Deep Field-aware Factorization Machine <br />
- 2019 (Sina) (Recsys) [FiBiNET] FiBiNET - combining feature importance and bilinear feature interaction for click-through rate prediction <br />
- 2019 (Tencent) (AAAI) [IFM] Interaction-aware Factorization Machines for Recommender Systems <br />
- 2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising <br />
- 2021 (Google) (WWW) * [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems <br />
- 2022 (Alibaba) (WSDM) * [CAN] CAN - Feature Co-Action Network for Click-Through Rate Prediction <br />
- 2023 (CIKM) * [GDCN] Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction <br />
- 2023 (Sina) (CIKM) [FiBiNet++] FiBiNet++ - Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction <br />
- 2023 (Sina) (CIKM) [MemoNet] MemoNet - Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction <br />
Longterm-Sequence-Modeling
- 2019 (Alibaba) (KDD) [MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction <br />
- 2019 (Google) (WWW) Towards Neural Mixture Recommender for Long Range Dependent User Sequences <br />
- 2020 (Alibaba) (Arxiv) ** [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction <br />
- 2020 (ICLR) Reformer - The Efficient Transformer <br />
- 2020 (SIGIR) [UBR4CTR] User Behavior Retrieval for Click-Through Rate Prediction <br />
- 2021 (Alibaba) (Arxiv) [ETA] End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model <br />
- 2022 (Alibaba) (Arxiv) ** [ETA] Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction <br />
- 2022 (Meituan) (CIKM) [SDIM] Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction <br />
- 2023 (Kuaishou) (Arixiv) [TWIN] TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou <br />
- 2023 (Kuaishou) (CIKM) [QIN] Query-dominant User Interest Network for Large-Scale Search Ranking <br />
- 2024 (Kuaishou) (CIKM) [TWINv2] TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou <br />
- 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction <br />
Loss
- 2024 (Tencent) (KDD) Understanding the Ranking Loss for Recommendation with Sparse User Feedback <br />
- 2024 (Tencent) (KDD) [BBP] Beyond Binary Preference - Leveraging Bayesian Approaches for Joint Optimization of Ranking and Calibration <br />
Multi-Modal
- 2018 (Alibaba) (CIKM) [Image CTR] Image Matters - Visually Modeling User Behaviors Using Advanced Model Server <br />
- 2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction <br />
Multi-domain-Multi-Scenario
- 2014 (TASLP) * [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation <br />
- 2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems <br />
- 2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts <br />
- 2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search <br />
- 2020 (Alibaba) (Arxiv) [SAML] Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce <br />
- 2020 (Alibaba) (CIKM) [HMoE] Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space <br />
- 2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction <br />
- 2020 (Tencent) (Recsys) ** [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations <br />
- 2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce <br />
- 2021 (Alibaba) (CIKM) ** [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction <br />
- 2022 (Alibaba) (CIKM) AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction <br />
- 2022 (Alibaba) (NIPS) ** [APG] APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction <br />
- 2023 (Alibaba) (CIKM) [HC2] Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking <br />
- 2023 (Alibaba) (CIKM) [MMN] Masked Multi-Domain Network - Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model <br />
- 2023 (Alibaba) (CIKM) [Rec4Ad] Rec4Ad - A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao <br />
- 2023 (Alibaba) (SIGIR) [MARIA] Multi-Scenario Ranking with Adaptive Feature Learning <br />
- 2023 (CIKM) [HAMUR] HAMUR - Hyper Adapter for Multi-Domain Recommendation <br />
- 2023 (Huawei) (CIKM) [DFFM] DFFM - Domain Facilitated Feature Modeling for CTR Prediction <br />
- 2023 (Kuaishou) (KDD) * [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information <br />
- 2023 (Tencent) (KDD) Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction <br />
- 2024 (Alibaba) (CIKM) * [MultiLoRA] MultiLoRA - Multi-Directional Low-Rank Adaptation for Multi-Domain Recommendation <br />
- 2024 (Alibaba) (RecSys) * [MLoRA] MLoRA - Multi-Domain Low-Rank Adaptive Network for Click-Through Rate Prediction <br />
- 2024 (Kuaishou) (SIGIR) [M3oE] M3oE - Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework <br />
- 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction <br />
- 2024 (WSDM) Exploring Adapter-based Transfer Learning for Recommender Systems - Empirical Studies and Practical Insights <br />
Multi-task
- (2018) (ICML) GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks <br />
- 2014 (TASLP) [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation <br />
- 2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer <br />
- 2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks <br />
- 2018 (Alibaba) (SIGIR) [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate <br />
- 2018 (CVPR) Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics <br />
- 2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts <br />
- 2019 (Alibaba) (CIKM) Multi-task based Sales Predictions for Online Promotions <br />
- 2019 (Alibaba) (Recys) A Pareto-Eficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation <br />
- 2019 (Google) (AAAI) SNR Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning <br />
- 2019 (Google) (Recsys) ** [Youtube Multi-task] Recommending what video to watch next - a multitask ranking system <br />
- 2019 (NIPS) Pareto Multi-Task Learning <br />
- 2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction <br />
- 2020 (Alibaba) (WWW) Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning <br />
- 2020 (Amazon) (WWW) Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation <br />
- 2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams <br />
- 2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems <br />
- 2020 (Tencent) (Recsys) ** [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations <br />
- 2021 (Alibaba) (SIGIR) [HM3] Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction <br />
- 2021 (Alibaba) (SIGIR) [MSSM] MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning <br />
- 2021 (Baidu) (SIGIR) [GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction <br />
- 2021 (Google) (Arxiv) [DSelect-k] DSelect-k Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning <br />
- 2021 (Google) (ICLR) HyperGrid Transformers - Towards A Single Model for Multiple Tasks <br />
- 2021 (Google) (KDD) Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning <br />
- 2021 (JD) (ICDE) Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint <br />
- 2021 (Kwai) (Arxiv) [POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems <br />
- 2021 (Meituan) (KDD) Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising <br />
- 2021 (Tencent) (Arxiv) Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling <br />
- 2021 (Tencent) (WWW) Personalized Approximate Pareto-Efficient Recommendation <br />
- 2022 (Google) (WWW) Can Small Heads Help? Understanding and Improving Multi-Task Generalization <br />
- 2023 (Airbnb) (KDD) Optimizing Airbnb Search Journey with Multi-task Learning <br />
- 2023 (Alibaba) (CIKM) [DTRN] Deep Task-specific Bottom Representation Network for Multi-Task Recommendation <br />
- 2023 (Google) (CIKM) Multitask Ranking System for Immersive Feed and No More Clicks - A Case Study of Short-Form Video Recommendation <br />
- 2023 (Google) (KDD) Improving Training Stability for Multitask Ranking Models in Recommender Systems <br />
- 2023 (Meta) (KDD) AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations <br />
- 2024 (Airbnb) (KDD) Multi-objective Learning to Rank by Model Distillation <br />
- 2024 (Kuaishou) (KDD) [GradCraft] GradCraft - Elevating Multi-task Recommendations through Holistic Gradient Crafting <br />
- 2024 (Kuaishou) [HoME] HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou <br />
- 2024 (Shopee) (KDD) [ResFlow] Residual Multi-Task Learner for Applied Ranking <br />
- 2024 (Tencent) (KDD) [STEM] Ads Recommendation in a Collapsed and Entangled World <br />
Pre-training
- 2019 (Alibaba) (IJCAI) [DeepMCP] Representation Learning-Assisted Click-Through Rate Prediction <br />
- 2019 (SIGIR) [BERT4Rec] (Alibaba) (SIGIR2019) BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer <br />
Sequence-Modeling
- 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations <br />
- 2017 (Google) (NIPS) ** Attention Is All You Need <br />
- 2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction <br />
- 2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks <br />
- 2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction <br />
- 2019 (Alibaba) (IJCAI) [DSIN] Deep Session Interest Network for Click-Through Rate Prediction <br />
- 2019 (Alibaba) (KDD) [BST] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba <br />
- 2019 (Alibaba) (KDD) [DSTN] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction <br />
- 2019 (Alibaba) (WWW) [TiSSA] TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors <br />
- 2019 (Tencent) (KDD) [RALM] TReal-time Attention Based Look-alike Model for Recommender System <br />
- 2020 (Alibaba) (SIGIR) [DHAN] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction <br />
- 2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive <br />
- 2020 (JD) (CIKM) **[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems <br />
- 2020 (JD) (NIPS) [KFAtt] Kalman Filtering Attention for User Behavior Modeling in CTR Prediction <br />
- 2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems <br />
- 2022 (Alibaba) (WSDM) Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search <br />
- 2022 (JD) (WWW) Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads <br />
- 2023 (JD) (CIKM) [IUI] IUI - Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction <br />
- 2023 (Meituan) (CIKM) [DCIN] Deep Context Interest Network for Click-Through Rate Prediction <br />
- 2023 (Pinterest) (KDD) TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest <br />
Trigger
04_Post-ranking
- 1998 (SIGIR) ** [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries <br />
- 2005 (WWW) Improving Recommendation Lists Through Topic Diversification <br />
- 2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation <br />
- 2009 (Microsoft) (WSDM) Diversifying Search Results <br />
- 2010 (WWW) Exploiting Query Reformulations for Web Search Result Diversification <br />
- 2016 (Amazon) (RecSys) Adaptive, Personalized Diversity for Visual Discovery <br />
- 2017 (Hulu) (NIPS) [DPP] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity <br />
- 2018 (Alibaba) (IJCAI) Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search <br />
- 2018 (Alibaba) (IJCAI) [Alibaba GMV] Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search <br />
- 2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes <br />
- 2018 (SIGIR) [DLCM] Learning a Deep Listwise Context Model for Ranking Refinement <br />
- 2019 (Alibaba) (WWW) [Value-based RL] Value-aware Recommendation based on Reinforcement Profit Maximization <br />
- 2019 (Alibaba) (KDD) [GAttN] Exact-K Recommendation via Maximal Clique Optimization <br />
- 2019 (Alibaba) (RecSys) ** [PRM] Personalized Re-ranking for Recommendation <br />
- 2019 (Google) (Arxiv) Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology <br />
- 2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns <br />
- 2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets <br />
- 2019 (Google) (WSDM) [Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System <br />
- 2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search <br />
- 2020 (Alibaba) (CIKM) [EdgeRec] EdgeRec - Recommender System on Edge in Mobile Taobao <br />
- 2020 (Huawei) (Arxiv) Personalized Re-ranking for Improving Diversity in Live Recommender Systems <br />
- 2021 (Alibaba) (Arxiv) [PRS] Revisit Recommender System in the Permutation Prospective <br />
- 2021 (Google) (WSDM) User Response Models to Improve a REINFORCE Recommender System <br />
- 2021 (Microsoft) Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective <br />
- 2023 (Amazon) (KDD) RankFormer - Listwise Learning-to-Rank Using Listwide Labels <br />
- 2023 (Meituan) (KDD) PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce <br />
- 2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation <br />
Seq2Slate
- 2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces <br />
- 2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions <br />
- 2017 (KDD) [DCM] Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction <br />
- 2018 (Microsoft) (EMNLP) [RL4NMT] A study of reinforcement learning for neural machine translation <br />
- 2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns <br />
05_Relevance
- 2020 (ICLR) [StructBERT] StructBERT - Incorporating Language Structures into Pre-training for Deep Language Understanding <br />
- 2021 (Alibaba) (WWW) Learning a Product Relevance Model from Click-Through Data in E-Commerce <br />
- 2023 (Meituan) (CIKM) [SPM] SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search <br />
06_Cascade
- 2023 (Alibaba) (CIKM) [COPR] COPR - Consistency-Oriented Pre-Ranking for Online Advertising <br />
- 2023 (Alibaba) (KDD) [ASMOL] Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System <br />
07_Large_Model
- 2019 (CIKM) [AutoInt] AutoInt -Automatic Feature Interaction Learning via Self-Attentive Neural Networks <br />
- 2020 (Arxiv) Scaling Laws for Neural Language Models <br />
- 2022 (Arxiv) (Meta) DHEN - A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction <br />
- 2024 (Arxiv) (Bytedance) [HLLM] HLLM - Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling <br />
- 2024 (Arxiv) ** (Meta) [GR] Actions Speak Louder than Words - Trillion-Parameter Sequential Transducers for Generative Recommendations <br />
- 2024 (PMLR) (Meta) [Wukong] Wukong - Towards a Scaling Law for Large-Scale Recommendation <br />
CV
- 2014 (ICML) [VAE] Auto-Encoding Variational Bayes <br />
- 2014 (NIPS) [GAN] Generative Adversarial Nets <br />
- 2017 (NIPS) [VQ-VAE] Neural Discrete Representation Learning <br />
- 2020 (NIPS) [Diffusion] Denoising Diffusion Probabilistic Models <br />
Deep_Learning
- 2012 (NIPS) [CNN] ImageNet Classification with Deep Convolutional Neural Networks <br />
- 2014 (JMLR) [Dropout] Dropout - A Simple Way to Prevent Neural Networks from Overfitting <br />
- 2015 (Google) (JMLR) [BatchNorm] Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift <br />
- 2015 (OpenAI) (ICLR) [Adam] Adam - A Method for Stochastic Optimization <br />
- 2016 (CVPR) [ResNet] Deep Residual Learning for Image Recognition <br />
- 2016 (OpenAI) (NIPS) [Weight Norm] Weight Normalization - A Simple Reparameterization to Accelerate Training of Deep Neural Networks <br />
- 2017 (Arxiv) [LayerNorm] Layer Normalization <br />
- 2017 (Google) (NIPS) [Transformer] Attention Is All You Need <br />
LLM
- 2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality <br />
- 2014 (Google) (NIPS) [Seq2Seq] Sequence to Sequence Learning with Neural Networks <br />
- 2017 (Google) (NIPS) [Transformer] Attention Is All You Need <br />
- 2017 (OpenAI) (NIPS) [RLHF] Deep Reinforcement Learning from Human Preferences <br />
- 2018 (OpenAI) (Arxiv) [GPT] Improving Language Understanding by Generative Pre-Training <br />
- 2019 (Google) (NAACL) [Bert] BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding <br />
- 2019 (OpenAI) (Arxiv) [GPT2] Language Models are Unsupervised Multitask Learners <br />
- 2020 (Arxiv) Scaling Laws for Neural Language Models <br />
- 2020 (OpenAI) (Arxiv) [GPT3] Language Models are Few-Shot Learners <br />
- 2021 (Microsoft) (Arxiv) [LoRA] LoRA - Low-Rank Adaptation of Large Language Models <br />
- 2022 (Google) (JMLR) [SwitchTransfomers] Switch Transformers - Scaling to Trillion Parameter Models with Simple and Efficient Sparsity <br />
- 2022 (Google) (NIPS) [ChainOfThought] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models <br />
- 2022 (Google) (TMLR) [Emergent] Emergent Abilities of Large Language Models <br />
- 2022 (OpenAI) (Arxiv) [InstructGPT] Training language models to follow instructions with human feedback <br />
- 2023 (Meta) (Arxiv) [LLaMA] LLaMA - Open and Efficient Foundation Language Models <br />
- 2023 (OpenAI) (Arxiv) [GPT4] GPT-4 Technical Report <br />
MOE
- 2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer <br />
- 2018 (Google) (KDD) ** [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts <br />
- 2022 (Google) (JMLR) [SwitchTransfomers] Switch Transformers - Scaling to Trillion Parameter Models with Simple and Efficient Sparsity <br />
- 2022 (Meta) (EMNLP) Efficient Large Scale Language Modeling with Mixtures of Experts <br />
- 2024 (Google) (ICLR) From Sparse to Soft Mixtures of Experts <br />
Multimodal
- 2020 (Google) (ICLR) [ALBERT] ALBERT - A Lite BERT for Self-supervised Learning of Language Representations <br />
- 2021 (Google) (ICLR) [VIT] An Image is Worth 16x16 Words - Transformers for Image Recognition at Scale <br />
- 2021 (OpenAI) (ICML) [CLIP] Learning Transferable Visual Models From Natural Language Supervision <br />
Self_Supervised_Learning
- 2020 (Alibaba) (AAAI) [DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction <br />
- 2020 (Alibaba) (CIKM) [BERT4Rec] BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer <br />
- 2020 (Alibaba) (KDD) Disentangled Self-Supervision in Sequential Recommenders <br />
- 2020 (Arxiv) UserBERT - Self-supervised User Representation Learning <br />
- 2020 (Arxiv) [SGL] Self-supervised Graph Learning for Recommendation <br />
- 2020 (CIKM) [S3Rec] S3-Rec - Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization <br />
- 2020 (EMNLP) [PTUM] PTUM - Pre-training User Model from Unlabeled User Behaviors via Self-supervision <br />
- 2020 (SIGIR) Self-Supervised Reinforcement Learning for Recommender Systems <br />
- 2020 (Xiangnan He) (Arxiv) Self-supervised Graph Learning for Recommendation <br />
- 2021 (Alibaba) (Arxiv) [CLRec] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems <br />
- 2021 (Alibaba) (CIKM) * [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce <br />
- 2021 (Alibaba) (WWW) Contrastive Pre-training for Sequential Recommendation <br />
- 2021 (Google) (CIKM) Self-supervised Learning for Large-scale Item Recommendations <br />
- 2021 (WSDM) [Prop] PROP - Pre-training with Representative Words Prediction for Ad-hoc Retrieval <br />
08_Transfer_Learning
- 2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network <br />
- 2015 (ICLR) [Fitnets] Fitnets - Hints for thin deep nets <br />
- 2018 (Alibaba) (AAAI) [Rocket] Rocket launching - A universal and efficient framework for training well-performing light net <br />
- 2018 (KDD)[Ranking Distillation] Ranking distillation - Learning compact ranking models with high performance for recommender system <br />
- 2019 (ICCV) [RCO] Knowledge Distillation via Route Constrained Optimization <br />
- 2020 (Alibaba) (KDD) *[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations <br />
Cross-domain
- 2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems <br />
- 2016 (JMLR) Domain-Adversarial Training of Neural Networks <br />
- 2018 (CIKM) CoNet - Collaborative Cross Networks for Cross-Domain Recommendation <br />
- 2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search <br />
- 2019 (Alibaba) (KDD) [MGTL] A minimax game for instance based selective transfer learning <br />
- 2019 (CIKM) DTCDR - A Framework for Dual-Target Cross-Domain Recommendation <br />
- 2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction <br />
- 2020 (WSDM) DDTCDR - Deep Dual Transfer Cross Domain Recommendation <br />
Meta-Learning
- 2019 (Alibaba) (KDD) [s_2Meta] Sequential Scenario-Specific Meta Learner for Online Recommendation <br />
- 2020 (Kuaishou) (SIGIR) [SML] How to Retrain Recommender System? A Sequential Meta-Learning Method <br />
Transfer
- 2018 (CVPR) Efficient parametrization of multi-domain deep neural networks <br />
- 2019 (ICML) Parameter-efficient transfer learning for NLP <br />
- 2020 (Tencent) (SIGIR) [PeterRec] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation <br />
09_Reinforcement_Learning
- 2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation <br />
- 2018 (Alibaba) (KDD) Reinforcement Learning to Rank in E-Commerce Search Engine Formalization, Analysis, and Application <br />
- 2018 (Spotify) (Recsys) [Spotify Bandit] Explore, Exploit, and Explain Personalizing Explainable Recommendations with Bandits <br />
- 2018 [Microsoft] (WWW) [DRN] DRN - A Deep Reinforcement Learning Framework for News Recommendation <br />
- 2019 (Alibaba) (WWW) [HRL] Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning <br />
- 2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets <br />
- 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System <br />
- 2019 (JD) (KDD) [FeedRec] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems <br />
- 2019 (Sigweb) Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey <br />
- 2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise <br />
- 2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering <br />
Conference
KDD2023
- 2023 (Airbnb) (KDD) Optimizing Airbnb Search Journey with Multi-task Learning <br />
- 2023 (Alibaba) (KDD) Capturing Conversion Rate Fluctuation during Sales Promotions - A Novel Historical Data Reuse Approach <br />
- 2023 (Amazon) (KDD) RankFormer - Listwise Learning-to-Rank Using Listwide Labels <br />
- 2023 (Baidu) (KDD) Learning Discrete Document Representations in Web Search <br />
- 2023 (Baidu) (KDD) S2phere - Semi-Supervised Pre-training for Web Search over Heterogeneous Learning to Rank Data <br />
- 2023 (Google) (KDD) Improving Training Stability for Multitask Ranking Models in Recommender Systems <br />
- 2023 (Kuaishou) (KDD) Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation <br />
- 2023 (Kuaishou) (KDD) [PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information <br />
- 2023 (Meituan) (KDD) PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce <br />
- 2023 (Meta) (KDD) AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations <br />
- 2023 (Microsoft) (KDD) Unifier - A Unified Retriever for Large-Scale Retrieval <br />
- 2023 (Pinterest) (KDD) TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest <br />
- 2023 (Tencent) (KDD) Binary Embedding-based Retrieval at Tencent <br />
- 2023 (Tencent) (KDD) CT4Rec - Simple yet Effective Consistency Training for Sequential Recommendation <br />
- 2023 (Tencent) (KDD) Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction <br />
KDD2024
- 2024 (Airbnb) (KDD) Multi-objective Learning to Rank by Model Distillation <br />
- 2024 (Bytedance) (KDD) [Trinity] Trinity - Syncretizing Multi-:Long-Tail:Long-Term Interests All in One <br />
- 2024 (Kuaishou) (KDD) [GradCraft] GradCraft - Elevating Multi-task Recommendations through Holistic Gradient Crafting <br />
- 2024 (Kuaishou) (KDD) [NAR4Rec] Non-autoregressive Generative Models for Reranking Recommendation <br />
- 2024 (Shopee) (KDD) [ResFlow] Residual Multi-Task Learner for Applied Ranking <br />
- 2024 (Tencent) (KDD) Understanding the Ranking Loss for Recommendation with Sparse User Feedback <br />
- 2024 (Tencent) (KDD) [BBP] Beyond Binary Preference - Leveraging Bayesian Approaches for Joint Optimization of Ranking and Calibration <br />
- 2024 (Tencent) (KDD) [LCN] Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction <br />
- 2024 (Tencent) (KDD) [STEM] Ads Recommendation in a Collapsed and Entangled World <br />
Corporation
- 2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network <br />
- 2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces <br />
- 2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions <br />
- 2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems <br />
- 2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations <br />
- 2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer <br />
- 2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes <br />
- 2018 (Google) (KDD) [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts <br />
- 2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns <br />
- 2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets <br />
- 2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets <br />
- 2019 (Google) (Recsys)[Youtube Multi-task] Recommending what video to watch next - a multitask ranking system <br />
- 2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System <br />
- 2020 (Google) (Arxiv) Self-supervised Learning for Large-scale Item Recommendations <br />
- 2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive <br />
- 2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams <br />
JDRecSys
- 2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems <br />
- 2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items <br />
- 2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering <br />
- 2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems <br />
TaobaoSearch
- 2018 (Alibaba) (IJCAI) Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search <br />
- 2018 (Alibaba) (IJCAI) [JUMP] JUMP - A Joint Predictor for User Click and Dwell Time <br />
- 2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks <br />
- 2018 (Alibaba) (WWW) [MA-RDPG] Learning to Collaborate - Multi-Scenario Ranking via Multi-Agent Reinforcement Learning <br />
- 2019 (Alibaba) (CIKM) Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search <br />
- 2019 (Alibaba) (KDD) [MGTL] A Minimax Game for Instance based Selective Transfer Learning <br />
- 2019 (Alibaba) (WWW) Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning <br />
- 2020 (Alibaba) (CIKM) [TIEN] Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction <br />
- 2020 (Alibaba) (NIPS) Neuron-level Structured Pruning using Polarization Regularizer <br />
- 2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction <br />
- 2021 (Alibaba) (AAAI) [ANPP] Attentive Neural Point Processes for Event Forecasting <br />
- 2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling <br />
- 2021 (Alibaba) (CIKM) [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce <br />
- 2021 (Alibaba) (KDD) [MGDSPR] Embedding-based Product Retrieval in Taobao Search <br />
- 2022 (Alibaba) (CIKM) [CLE-QR] Query Rewriting in TaoBao Search <br />
- 2022 (Alibaba) (CIKM) [MOPPR] Multi-Objective Personalized Product Retrieval in Taobao Search <br />
- 2023 (Alibaba) (KDD) [ASMOL] Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System <br />