Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
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Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural...
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer and Reinforcement Learning.
1_Embedding
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2013 (Google) (NIPS) [Word2vec] Distributed Representations of Words and Phrases and their Compositionality
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2014 (KDD) [DeepWalk] DeepWalk - online learning of social representations
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2015 (WWW) [LINE] LINE Large-scale Information Network Embedding
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2016 (KDD) [Node2vec] node2vec - Scalable Feature Learning for Networks
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2017 (ICLR) [GCN] Semi-supervised Classification with Graph Convolutional Networks
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2017 (KDD) [Struc2vec] struc2vec - Learning Node Representations from Structural Identity
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2017 (NIPS) [GraphSAGE] Inductive Representation Learning on Large Graphs
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2018 (Airbnb) (KDD) *[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb
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2018 (Alibaba) (KDD) *[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
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2018 (ICLR) [GAT] Graph Attention Networks
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2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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2018 (WSDM) [NetMF] Network embedding as matrix factorization - Unifying deepwalk, line, pte, and node2vec
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2019 (Alibaba) (KDD) *[GATNE] Representation Learning for Attributed Multiplex Heterogeneous Network
2_Matching
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2013 (Microsoft) (CIKM) [DSSM] Learning deep structured semantic models for web search using clickthrough data
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2015 (KDD) [Sceptre] Inferring Networks of Substitutable and Complementary Products
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2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
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2018 (KDD) *[TDM] (Alibaba) Learning Tree-based Deep Model for Recommender Systems
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2018 (Pinterest) (KDD) *[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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2019 (Alibaba) (CIKM) **[MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
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2019 (Alibaba) (CIKM) *[SDM] SDM - Sequential deep matching model for online large-scale recommender system
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2019 (Alibaba) (NIPS) *[JTM] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
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2019 (Amazon) (KDD) Semantic Product Search
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2019 (Baidu) (KDD) *[MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
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2019 (Google) (RecSys) Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
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2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
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2019 [Tencent] (KDD) A User-Centered Concept Mining System for Query and Document Understanding at Tencent
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2020 (Alibaba) (ICML) [OTM] Learning Optimal Tree Models under Beam Search
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2020 (Alibaba) (KDD) *[ComiRec] Controllable Multi-Interest Framework for Recommendation
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2020 (Facebook) (KDD) **[Embedding for Facebook Search] Embedding-based Retrieval in Facebook Search
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2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
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2020 (JD) (SIGIR) [DPSR] Towards Personalized and Semantic Retrieval - An End-to-EndSolution for E-commerce Search via Embedding Learning
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2020 (Microsoft) (Arxiv) TwinBERT - Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval
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2021 (Amazon) (KDD) Extreme Multi-label Learning for Semantic Matching in Product Search
3_Ranking
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2014 (ADKDD) (Facebook) Practical Lessons from Predicting Clicks on Ads at Facebook
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2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems
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2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
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2018 (Alibaba) (CIKM) [Image CTR] Image Matters - Visually Modeling User Behaviors Using Advanced Model Server
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2018 (Alibaba) (KDD) **[DIN] Deep Interest Network for Click-Through Rate Prediction
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2019 (Alibaba) (AAAI) **[DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
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2019 (Alibaba) (IJCAI) [DSIN] Deep Session Interest Network for Click-Through Rate Prediction
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2019 (Alibaba) (IJCAI) [DeepMCP] Representation Learning-Assisted Click-Through Rate Prediction
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2019 (Alibaba) (KDD) [DSTN] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
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2019 (Alibaba) (KDD) [MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
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2019 (Alibaba) (KDD)[BST] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
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2019 (Alibaba) (WWW) [TiSSA] TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors
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2019 (Facebook) (Arxiv) [DLRM] (Facebook) Deep Learning Recommendation Model for Personalization and Recommendation Systems, Facebook
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2019 (Google) (WWW) Towards Neural Mixture Recommender for Long Range Dependent User Sequences
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2019 (KDD) (Airbnb) Applying Deep Learning To Airbnb Search
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2019 (SIGIR) [BERT4Rec] (Alibaba) (SIGIR2019) BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer
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2019 (Tencent) (KDD) [RALM] TReal-time Attention Based Look-alike Model for Recommender System
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2020 (Airbnb) (KDD) Improving Deep Learning For Airbnb Search
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2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
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2020 (Alibaba) (Arxiv) [SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
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2020 (Alibaba) (SIGIR) [DHAN] Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
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2020 (Baidu) (KDD) [CAN] Combo-Attention Network for Baidu Video Advertising
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2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive
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2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
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2020 (JD) (NIPS) [KFAtt] Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
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2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems
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2021 (Alibaba) (CIKM) [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
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2022 (Alibaba) (WSDM) Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
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2022 (Alibaba) (WWW) Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
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2022 (JD) (WWW) Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
Classic
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2003 (Amazon) (IEEE) [CF] Amazon.com recommendations - Item-to-item collaborative filtering
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2009 (Computer) [MF] Matrix factorization techniques for recommender systems
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2016 (ICLR) [GRU4Rec] Session-based Recommendations with Recurrent Neural Networks
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2017 (Amazon) (IEEE) Two decades of recommender systems at Amazon.com
Cross
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2010 (ICDM) [FM] Factorization machines
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2013 (Google) (KDD) [LR] Ad Click Prediction - a View from the Trenches
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2016 (Arxiv) [PNN] Product-based Neural Networks for User Response Prediction
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2016 (Criteo) (Recsys) [FFM] Field-aware Factorization Machines for CTR Prediction
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2016 (ECIR) [FNN] Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction
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2016 (KDD) [Deepintent] Deepintent - Learning attentions for online advertising with recurrent neural networks
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2016 (Microsoft) (KDD) [Deep Crossing] Deep Crossing - Web-scale modeling without manually crafted combinatorial features
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2017 (Google) (ADKDD) [DCN] Deep & CrossNetwork for Ad Click Predictions
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2017 (Huawei) (IJCAI) [DeepFM] DeepFM - A Factorization-Machine based Neural Network for CTR Prediction
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2017 (IJCAI) [AFM] Attentional Factorization Machines Learning the Weight of Feature Interactions via Attention Networks
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2017 (SIGIR) [NFM] Neural Factorization Machines for Sparse Predictive Analytics
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2017 (WWW) [NCF] Neural Collaborative Filtering
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2018 (CVPR) [SENet] Squeeze-and-Excitation Networks
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2018 (Google) (WSDM) [Latent Cross] Latent Cross Making Use of Context in Recurrent Recommender Systems
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2018 (KDD) [xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems
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2018 (TOIS) [PNN] Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
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2019 (CIKM) [AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks
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2019 (Huawei) (WWW) [FGCNN] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
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2019 (Sina) (Arxiv) [FAT-DeepFFM] FAT-DeepFFM - Field Attentive Deep Field-aware Factorization Machine
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2019 (Sina) (Recsys) [FiBiNET] FiBiNET - combining feature importance and bilinear feature interaction for click-through rate prediction
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2019 (Tencent) (AAAI) [IFM] Interaction-aware Factorization Machines for Recommender Systems
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2021 (Google) (WWW) [DCN V2] DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
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2022 (Alibaba) (WSDM) [CAN] CAN - Feature Co-Action Network for Click-Through Rate Prediction
Delayed-Feedback
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2008 (KDD) Learning Classifiers from Only Positive and Unlabeled Data
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2014 (Criteo) (KDD) [DFM] Modeling Delayed Feedback in Display Advertising
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2018 (Arxiv) [NoDeF] A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
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2019 (Twitter) (RecSys) Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
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2020 (AdKDD) Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions
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2020 (JD) (IJCAI) [TS-DL] An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
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2020 (SIGIR) [DLA-DF] Dual Learning Algorithm for Delayed Conversions
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2020 (WWW) [FSIW] A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
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2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
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2021 (Alibaba) (AAAI) [ESDF] Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
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2021 (Alibaba) (Arxiv) [Defer] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling
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2021 (Google) (Arxiv) Handling many conversions per click in modeling delayed feedback
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2021 (Tencent) (SIGIR) Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback
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2022 (Alibaba) (WWW) [DEFUSE] Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
4_Post-ranking
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1998 (SIGIR) [MRR] The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
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2005 (WWW) Improving Recommendation Lists Through Topic Diversification
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2008 (SIGIR) [α-NDCG] Novelty and Diversity in Information Retrieval Evaluation
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2009 (Microsoft) (WSDM) Diversifying Search Results
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2010 (WWW) Exploiting Query Reformulations for Web Search Result Diversification
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2016 (Amazon) (RecSys) Adaptive, Personalized Diversity for Visual Discovery
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2017 (Hulu) (NIPS) [DPP] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
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2018 (Alibaba) (IJCAI) [Alibaba GMV] Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
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2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes
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2018 (SIGIR) [DLCM] Learning a Deep Listwise Context Model for Ranking Refinement
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2019 (Alibaba) (WWW) [Value-based RL] Value-aware Recommendation based on Reinforcement Profit Maximization
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2019 (Alibaba) (KDD) [GAttN] Exact-K Recommendation via Maximal Clique Optimization
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2019 (Alibaba) (RecSys) [PRM] Personalized Re-ranking for Recommendation
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2019 (Google) (Arxiv) Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology
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2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns
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2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
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2019 (Google) (WSDM) [Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
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2020 (Airbnb) (KDD) Managing Diversity in Airbnb Search
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2020 (Alibaba) (CIKM) [EdgeRec] EdgeRec - Recommender System on Edge in Mobile Taobao
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2020 (Huawei) (Arxiv) Personalized Re-ranking for Improving Diversity in Live Recommender Systems
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2021 (Alibaba) (Arxiv) [PRS] Revisit Recommender System in the Permutation Prospective
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2021 (Google) (WSDM) User Response Models to Improve a REINFORCE Recommender System
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2021 (Microsoft) Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective
Seq2Slate
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2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces
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2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
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2017 (KDD) [DCM] Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction
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2018 (Microsoft) (EMNLP) [RL4NMT] A study of reinforcement learning for neural machine translation
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2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns
5_Multi-task
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2014 (TASLP) [LHUC] Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
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2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer
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2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
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2018 (Alibaba) (SIGIR) [ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate
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2018 (CVPR) Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
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2018 (Google) (KDD) [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
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2019 (Alibaba) (CIKM) Multi-task based Sales Predictions for Online Promotions
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2019 (Alibaba) (Recys) A Pareto-Eficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation
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2019 (Google) (AAAI) SNR Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
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2019 (Google) (Recsys)[Youtube Multi-task] Recommending what video to watch next - a multitask ranking system
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2019 (NIPS) Pareto Multi-Task Learning
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2020 (Alibaba) (SIGIR) [ESM2] Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
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2020 (Alibaba) (WWW) Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
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2020 (Amazon) (WWW) Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation
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2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams
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2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
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2020 (Tencent) (Recsys) [PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
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2021 (Alibaba) (SIGIR) [HM3] Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
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2021 (Alibaba) (SIGIR) [MSSM] MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning
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2021 (Baidu) (SIGIR) [GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction
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2021 (Google) (Arxiv) [DSelect-k] DSelect-k Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
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2021 (Google) (ICLR) HyperGrid Transformers - Towards A Single Model for Multiple Tasks
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2021 (Google) (KDD) Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
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2021 (JD) (ICDE) Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
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2021 (Kwai) (Arxiv) [POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems
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2021 (Meituan) (KDD) Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
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2021 (Tencent) (Arxiv) Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
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2021 (Tencent) (WWW) Personalized Approximate Pareto-Efficient Recommendation
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2022 (Google) (WWW) Can Small Heads Help? Understanding and Improving Multi-Task Generalization
6_Graph_Neural_Networks
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2018 (Pinterest) (KDD) [PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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2019 (Alibaba) (KDD) [IntentGC] IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
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2019 (Alibaba) (KDD) [MEIRec] Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
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2019 (Alibaba) (SIGIR) [GIN] Graph Intention Network for Click-through Rate Prediction in Sponsored Search
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2020 (Alibaba) (SIGIR) [ATBRG] ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
7_Transfer_Learning
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2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network
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2015 (ICLR) [Fitnets] Fitnets - Hints for thin deep nets
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2018 (Alibaba) (AAAI) [Rocket] Rocket launching - A universal and efficient framework for training well-performing light net
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2018 (KDD)[Ranking Distillation] Ranking distillation - Learning compact ranking models with high performance for recommender system
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2019 (ICCV) [RCO] Knowledge Distillation via Route Constrained Optimization
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2020 (Alibaba) (KDD) *[Privileged Features Distillation] Privileged Features Distillation at Taobao Recommendations
Cross-domain
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2015 (Microsoft) (WWW) A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
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2016 (JMLR) Domain-Adversarial Training of Neural Networks
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2018 (CIKM) CoNet - Collaborative Cross Networks for Cross-Domain Recommendation
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2019 (Alibaba) (CIKM) [WE-CAN] Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search
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2019 (Alibaba) (KDD) [MGTL] A minimax game for instance based selective transfer learning
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2019 (CIKM) DTCDR - A Framework for Dual-Target Cross-Domain Recommendation
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2020 (Alibaba)(CIKM) [MiNet] MiNet - Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
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2020 (WSDM) DDTCDR - Deep Dual Transfer Cross Domain Recommendation
Meta-Learning
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2019 (Alibaba) (KDD) [s_2Meta] Sequential Scenario-Specific Meta Learner for Online Recommendation
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2020 (Kuaishou) (SIGIR) [SML] How to Retrain Recommender System? A Sequential Meta-Learning Method
Multi-Scenario
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2020 (Alibaba) (Arxiv) [SAML] Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
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2020 (Alibaba) (CIKM) [HMoE] Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space
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2021 (Alibaba) (Arxiv) [STAR] One Model to Serve All - Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
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2021 (Alibaba) (CIKM) [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
Transfer
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2018 (CVPR) Efficient parametrization of multi-domain deep neural networks
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2019 (ICML) Parameter-efficient transfer learning for NLP
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2020 (Tencent) (SIGIR) [PeterRec] Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
8_Reinforcement_Learning
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2010 (Yahoo) (WWW) [LinUCB] A Contextual-Bandit Approach to Personalized News Article Recommendation
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2018 (Alibaba) (KDD) Reinforcement Learning to Rank in E-Commerce Search Engine Formalization, Analysis, and Application
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2018 (Alibaba) (WWW) [MA-RDPG] Learning to Collaborate Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
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2018 (JD) (KDD) [DEERS] Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
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2018 (JD) (RecSys) [DeepPage] Deep Reinforcement Learning for Page-wise Recommendations
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2018 (KDD) [Robust DQN] Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation
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2018 (Spotify) (Recsys) [Spotify Bandit] Explore, Exploit, and Explain Personalizing Explainable Recommendations with Bandits
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2018 [Microsoft] (WWW) [DRN] DRN - A Deep Reinforcement Learning Framework for News Recommendation
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2019 (Alibaba) (WWW) [HRL] Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
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2019 (DRL4KDD) [LIRD] Deep Reinforcement Learning for List-wise Recommendations
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2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
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2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
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2019 (JD) (KDD) [FeedRec] Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
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2019 (Sigweb) Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey
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2020 (Baidu) (CIKM) [DeepChain] Whole-Chain Recommendations
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2020 (Bytedance) (KDD) [RAM] Jointly Learning to Recommend and Advertise
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2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering
9_Self_Supervised_Learning
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2020 (Alibaba) (AAAI) [DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction
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2020 (Alibaba) (CIKM) [BERT4Rec] BERT4Rec - Sequential Recommendation with Bidirectional Encoder Representations from Transformer
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2020 (Alibaba) (KDD) Disentangled Self-Supervision in Sequential Recommenders
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2020 (Arxiv) UserBERT - Self-supervised User Representation Learning
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2020 (Arxiv) [SGL] Self-supervised Graph Learning for Recommendation
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2020 (CIKM) [S3Rec] S3-Rec - Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
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2020 (EMNLP) [PTUM] PTUM - Pre-training User Model from Unlabeled User Behaviors via Self-supervision
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2020 (SIGIR) Self-Supervised Reinforcement Learning for Recommender Systems
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2020 (Xiangnan He) (Arxiv) Self-supervised Graph Learning for Recommendation
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2021 (Alibaba) (Arxiv) [CLRec] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
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2021 (Alibaba) (CIKM) [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
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2021 (Alibaba) (WWW) Contrastive Pre-training for Sequential Recommendation
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2021 (Google) (CIKM) Self-supervised Learning for Large-scale Item Recommendations
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2021 (WSDM) [Prop] PROP - Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Conference
2022
Corporation
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2014 (Google) (NIPS) [Knoledge Distillation] Distilling the Knowledge in a Neural Network
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2015 (Google) (Arxiv) Deep Reinforcement Learning in Large Discrete Action Spaces
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2015 (Google) (Arxiv) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
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2016 (Google) (DLRS) **[Wide & Deep] Wide & Deep Learning for Recommender Systems
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2016 (Google) (RecSys) **[Youtube DNN] Deep Neural Networks for YouTube Recommendations
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2017 (Google) (ICLR) [Sparsely-Gated MOE] Outrageously large neural networks - The sparsely-gated mixture-of-experts layer
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2018 (Google) (CIKM) [DPP] Practical Diversified Recommendations on YouTube with Determinantal Point Processes
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2018 (Google) (KDD) [MMoE] Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
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2019 (Google) (Arxiv) Seq2slate - Re-ranking and slate optimization with rnns
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2019 (Google) (IJCAI) *[SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
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2019 (Google) (IJCAI) [SlateQ] SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
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2019 (Google) (Recsys)[Youtube Multi-task] Recommending what video to watch next - a multitask ranking system
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2019 (Google) (WSDM) *[Top-K Off-Policy] Top-K Off-Policy Correction for a REINFORCE Recommender System
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2020 (Google) (Arxiv) Self-supervised Learning for Large-scale Item Recommendations
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2020 (Google) (KDD) [Google Drive] Improving Recommendation Quality in Google Drive
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2020 (Google) (KDD) [MoSE] Multitask Mixture of Sequential Experts for User Activity Streams
JDRecSys
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2020 (JD) (CIKM) *[DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
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2020 (JD) (CIKM) *[DecGCN] Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
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2020 (JD) (SIGIR) [NICF] Neural Interactive Collaborative Filtering
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2020 (JD) (WSDM) [HUP] Hierarchical User Profiling for E-commerce Recommender Systems
TaobaoSearch
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2018 (Alibaba) (IJCAI) Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
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2018 (Alibaba) (IJCAI) [JUMP] JUMP - A Joint Predictor for User Click and Dwell Time
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2018 (Alibaba) (KDD) [DUPN] Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
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2018 (Alibaba) (WWW) [MA-RDPG] Learning to Collaborate - Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
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2019 (Alibaba) (CIKM) Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search
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2019 (Alibaba) (KDD) [MGTL] A Minimax Game for Instance based Selective Transfer Learning
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2019 (Alibaba) (WWW) Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
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2020 (Alibaba) (CIKM) [TIEN] Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction
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2020 (Alibaba) (NIPS) Neuron-level Structured Pruning using Polarization Regularizer
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2020 (Alibaba) (WWW) [MARN] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
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2021 (Alibaba) (AAAI) [ANPP] Attentive Neural Point Processes for Event Forecasting
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2021 (Alibaba) (AAAI) [ES-DFM] Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
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2021 (Alibaba) (CIKM) [ZEUS] Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
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Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural...