(English Version is Here)

1. 截至2022年10月08日,本仓库收集汇总了推荐系统领域相关论文共420篇,涉及:召回,粗排,精排,重排,多任务,多场景,多模态,冷启动,校准, 纠偏,多样性,公平性,反馈延迟,蒸馏,对比学习,因果推断,Look-Alike,Learning-to-Rank,强化学习等领域,本仓库会跟踪业界进展,持续更新。
2. 因文件名特殊字符的限制,故论文title中所有的:都改为了-,检索时请注意。
3. 文件名前缀中带有[]的,表明本人已经通读过,第一个[]中为论文年份,第二个[]中为发表机构或公司(可选),第三个[]中为论文提出的model或method的简称(可选)。
4. 在某些一级分类下面,还有若干二级分类;一篇论文可能应该涉及多个二级分类(例如用对比学习的方法做召回),最终我会将论文放在较主要的那一类下;分类也会随时调整优化,欢迎在issue中提出宝贵意见。
5. 若您是文章作者,且不希望您的论文出现在这里,请在issue中提出,我核实后会马上下架。
6. 关于排序算法的一些实现,请见我的另一个repo: https://github.com/tangxyw/RecAlgorithm
7. 本仓库仅供交流学习使用,不做任何商业目的。

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• Rank
• Industry
• Pre-Rank
• Re-Rank
• Match
• Multi-Task
• Multi-Modal
• Multi-Scenario
• Debias
• Calibration
• Distillation
• Feedback-Delay
• ContrastiveLearning
• Cold-Start
• Learning-to-Rank
• Fairness
• Look-Alike
• CausalInference
• Diversity
• ABTest
• Reinforce

• [2009][BPR] Bayesian Personalized Ranking from Implicit Feedback
• [2010][FM] Factorization Machines
• [2014][Facebook][GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook
• [2016][UCL][FNN] Deep Learning over Multi-field Categorical Data
• [2016][Microsft][Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features
• [2016][Google][Wide&Deep] Wide & Deep Learning for Recommender Systems
• [2016][SJTU][PNN] Product-based Neural Networks for User Response Prediction
• [2016][NTU][FFM] Field-aware Factorization Machines for CTR Prediction
• [2017][Stanford][DCN] Deep & Cross Network for Ad Click Predictions
• [2017][NUS][NFM] Neural Factorization Machines for Sparse Predictive Analytics
• [2017][ZJU][AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks
• [2017][NUS][NCF] Neural Collaborative Filtering
• [2017][Alibaba][MLR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
• [2017][Huawei][DeepFM] A Factorization-Machine based Neural Network for CTR Prediction
• [2018][USTC][xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems
• [2019][AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks
• [2020][Tencent][DFN] Deep Feedback Network for Recommendation
• SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS

• [2016][Youtube] Deep Neural Networks for YouTube Recommendations
• [2016][Microsoft] User Fatigue in Online News Recommendation
• [2017][Alibaba][DIN] Deep Interest Network for Click-Through Rate Prediction
• [2017][Alibaba][ATRank] ATRank - An Attention-Based User Behavior Modeling Framework for Recommendation
• [2018][Alibaba][DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction
• [2018][FwFM] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
• [2018][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb
• [2019][Alibaba][DSIN] Deep Session Interest Network for Click-Through Rate Prediction
• [2019][Alibaba][BST] Behavior Sequence Transformer for E-commerceRecommendation in Alibaba
• [2019][Weibo][FiBiNET] FiBiNET - Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
• [2019][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
• [2019][Airbnb] Applying Deep Learning To Airbnb Search
• [2020][Alibaba][CAN] CAN - Revisiting Feature Co-Action for Click-Through Rate Prediction
• [2020][Alibaba][ESAM] ESAM - Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
• [2020][Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
• [2020][Alibaba][DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction
• [2021][Fliggy] [DMSN] Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
• [2021][Huawei][AutoDis] An Embedding Learning Framework for Numerical Features in CTR Prediction
• [2021][Alibaba][DINMP] A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction
• [2021][Google] Bootstrapping Recommendations at Chrome Web Store
• Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
• Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
• Curriculum Disentangled Recommendation with Noisy Multi-feedback
• Category-Specific CNN for Visual-aware CTR Prediction at JD.com
• ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
• CAEN - A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
• Dual Graph enhanced Embedding Neural Network for CTR Prediction
• Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
• Deep Learning Recommendation Model for Personalization and Recommendation System
• Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
• Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
• Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
• Denoising User-aware Memory Network for Recommendation
• EXTR - Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search
• End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model
• FM2 - Field-matrixed Factorization Machines for Recommender Systems
• FeedRec - News Feed Recommendation with Various User Feedbacks
• Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
• FLEN - Leveraging Field for Scalable CTR Prediction
• FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction
• GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction
• Hybrid Interest Modeling for Long-tailed Users
• HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction
• Improving Recommendation Quality in Google Drive
• Improving Deep Learning For Airbnb Search
• Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
• Long Short-Term Temporal Meta-learning in Online Recommendation
• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
• Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
• MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
• Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
• MRIF - Multi-resolution Interest Fusion for Recommendation
• News Recommendation with Candidate-aware User Modeling
• Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
• PURS - Personalized Unexpected Recommender System for Improving User Satisfaction
• Recommender Transformers with Behavior Pathways
• Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
• Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
• Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
• Triangle Graph Interest Network for Click-through Rate Prediction
• TencentRec - Real-time Stream Recommendation in Practice
• TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors
• User Behavior Retrieval for Click-Through Rate Prediction

• Modeling Two-Way Selection Preference for Person-Job Fit
• MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING

• KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
• KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems

• Bundle Recommendation with Graph Convolutional Networks
• CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation
• Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

• Real-time Short Video Recommendation on Mobile Devices

• [2020][meituan][STGCN] STGCN - A Spatial-Temporal Aware Graph Learning Method for POI Recommendation
• A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations
• A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
• Empowering Next POI Recommendation with Multi-Relational Modeling
• Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
• LightMove - A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
• Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
• Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
• Online POI Recommendation - Learning Dynamic Geo-Human Interactions in Streams
• Point-of-Interest Recommender Systems based on Location-Based Social Networks - A Survey from an Experimental Perspective
• POINTREC - A Test Collection for Narrative-driven Point of Interest Recommendation
• ST-PIL - Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation
• TADSAM - A Time-Aware Dynamic Self-Attention Model for Next Point-of-Interest Recommendation
• Why We Go Where We Go - Profiling User Decisions on Choosing POIs
• Where to Go Next - Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation
• You Are What and Where You Are - Graph Enhanced Attention Network for Explainable POI Recommendation

• Automatically Discovering User Consumption Intents in Meituan
• FINN - Feedback Interactive Neural Network for Intent Recommendation
• Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

• [2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest

• [2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation
• Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
• Feature Hashing for Large Scale Multitask Learning
• Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks
• Hash Embeddings for Efficient Word Representations
• Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems

• Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation

• [2020][Alibaba][COLD] COLD - Towards the Next Generation of Pre-Ranking System
• [2021][Alibaba] Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking - A Learnable Feature Selection based Approach
• [2022] On Ranking Consistency of Pre-ranking Stage
• AutoFAS - Automatic Feature and Architecture Selection for Pre-Ranking System
• Cascade Ranking for Operational E-commerce Search
• Contrastive Information Transfer for Pre-Ranking Systems
• EENMF - An End-to-End Neural Matching Framework for E-Commerce Sponsored Search

• [2018][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
• [2020][LinkedIn] Ads Allocation in Feed via Constrained Optimization
• Cross DQN - Cross Deep Q Network for Ads Allocation in Feed
• Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems
• GenDeR - A Generic Diversified Ranking Algorithm
• GRN - Generative Rerank Network for Context-wise Recommendation
• Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
• Learning a Deep Listwise Context Model for Ranking Refinement
• Neural Re-ranking in Multi-stage Recommender Systems - A Review
• Practical Diversified Recommendations on YouTube with Determinantal Point Processes
• Personalized Click Shaping through Lagrangian Duality for Online Recommendation
• Personalized Re-ranking for Recommendation
• Personalized Re-ranking with Item Relationships for E-commerce
• Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
• Revisit Recommender System in the Permutation Prospective
• SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
• Seq2slate - Re-ranking and slate optimization with rnns
• Sliding Spectrum Decomposition for Diversified Recommendation
• The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
• User Response Models to Improve a REINFORCE Recommender System

• [2015][Microsoft][DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
• [2016][Yahoo][App2Vec] App2Vec - Vector Modeling of Mobile Apps and Applications
• [2018][TC-CML] Loss Aversion in Recommender Systems - Utilizing Negative User Preference to Improve Recommendation Quality
• [2019][Google] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
• [2019][Baidu][MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search
• [2019][Alibaba][SDM] SDM - Sequential Deep Matching Model for Online Large-scale Recommender System
• [2020][Alibaba][Swing&Surprise] Large Scale Product Graph Construction for Recommendation in E-commerce
• [2020][Weixin][UTPM] Learning to Build User-tag Profile in Recommendation System
• [2020][Facebook][EBR] Embedding-based Retrieval in Facebook Search
• [2020][Google][MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
• [2021][Google] Self-supervised Learning for Large-scale Item Recommendations
• [2021][Alibaba][MGDSPR] Embedding-based Product Retrieval in Taobao Search
• A User-Centered Concept Mining System for Query and Document Understanding at Tencent
• A Dual Augmented Two-tower Model for Online Large-scale Recommendation
• CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems
• Cross-Batch Negative Sampling for Training Two-Tower Recommenders
• Disentangled Self-Supervision in Sequential Recommenders
• Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations
• Efficient Training on Very Large Corpora via Gramian Estimation
• Extreme Multi-label Learning for Semantic Matching in Product Search
• Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model
• Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
• Itinerary-aware Personalized Deep Matching at Fliggy
• Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
• Learning Tree-based Deep Model for Recommender Systems
• Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
• Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates
• Path-based Deep Network for Candidate Item Matching in Recommenders
• Sparse-Interest Network for Sequential Recommendation
• Self-Attentive Sequential Recommendation
• Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning
• Uni-Retriever - Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
• XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

• Truncation-Free Matching System for Display Advertising at Alibaba

• Collaborative Filtering Recommender Systems
• GroupLens - An open architecture for collaborative filtering of Netnews
• Item-Based Collaborative Filtering Recommendation Algorithms

• [2019][Alibaba][MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
• [2020][Alibaba][ComiRec] Controllable Multi-Interest Framework for Recommendation
• Every Preference Changes Differently - Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation
• Improving Multi-Interest Network with Stable Learning
• Multiple Interest and Fine Granularity Network for User Modeling

• [2014][word2vec] Negative-Sampling Word-Embedding Method
• [2014][DeepWalk] DeepWalk - Online Learning of Social Representations
• [2015][Microsoft][LINE] LINE - Large-scale Information Network Embedding
• [2016][SDNE] Structural Deep Network Embedding
• [2016][Stanford][node2vec] node2vec - Scalable Feature Learning for Networks
• [2016][word2vec] word2vec Parameter Learning Explained
• [2016][item2vec] ITEM2VEC - NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING
• [2017][GCN] SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
• [2017][Stanford][GraphSage] Inductive Representation Learning on Large Graphs
• [2018][GAT] GRAPH ATTENTION NETWORKS
• [2018][Alibaba] Learning and Transferring IDs Representation in E-commerce
• [2018][Pinterest][PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• [2018][Etsy] Learning Item-Interaction Embeddings for User Recommendations
• [2018][Alibaba][EGES] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
• [2019][SR-GNN] Session-based Recommendation with Graph Neural Networks
• [2019][NGCF]Neural Graph Collaborative Filtering
• [2020][LightGCN] LightGCN - Simplifying and Powering Graph Convolution Network for Recommendation
• ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
• Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
• Disentangled Graph Collaborative Filtering
• Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items
• Embedding-based News Recommendationfor Millions of Users
• Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
• Friend Recommendations with Self-Rescaling Graph Neural Networks
• Graph Convolutional Matrix Completion
• Graph Neural Networks for Friend Ranking in Large-scale Social Platforms
• Graph Intention Network for Click-through Rate Prediction in Sponsored Search
• Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation
• Graph Neural Networks for Social Recommendation
• GraphSAIL - Graph Structure Aware Incremental Learning for Recommender Systems
• IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
• MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
• Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec
• Neighbor Interaction Aware Graph Convolution Networks for Recommendation
• Package Recommendation with Intra- and Inter-Package Attention Networks
• PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest
• ProNE - Fast and Scalable Network Representation Learning
• Representation Learning for Attributed Multiplex Heterogeneous Network
• Revisiting Item Promotion in GNN-based Collaborative Filtering - A Masked Targeted Topological Attack Perspective
• Self-supervised Graph Learning for Recommendation
• SVD-GCN - A Simplified Graph Convolution Paradigm for Recommendation
• Self-Supervised Hypergraph Transformer for Recommender Systems
• TwHIN - Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
• metapath2vec - Scalable Representation Learning for Heterogeneous Networks
• struc2vec - Learning Node Representations from Structural Identity

• [2012][MGDA] Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
• [2018][Alibaba][ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate
• [2018][MagicLeap][GradNorm] GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
• [2018][Google][MMOE] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
• [2018][Cambridge] Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
• [2019][Alibaba][DBMTL] Deep Bayesian Multi-Target Learning for Recommender Systems
• [2019][Intel] Multi-Task Learning as Multi-Objective Optimization
• [2019][Youtube] Recommending What Video to Watch Next - A Multitask Ranking System
• [2019][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation
• [2020][Alibaba][Multi-IPW&Multi-DR] LARGE-SCALE CAUSAL APPROACHES TO DEBIASING POST-CLICK CONVERSION RATE ESTIMATION WITH MULTI-TASK LEARNING
• [2020][Tencent][PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
• [2020][Google][MoSE] Multitask Mixture of Sequential Experts for User Activity Streams
• [2020][JD][DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
• [2020][PCGrad] Gradient Surgery for Multi-Task Learning
• [2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
• [2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
• Can Small Heads Help Understanding and Improving Multi-Task Generalization
• DSelect-k - Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
• Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
• GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction
• Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
• HyperGrid Transformers - Towards A Single Model for Multiple Tasks
• MetaBalance - Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
• MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning
• Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
• Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation
• Multi-Task Learning for Dense Prediction Tasks - A Survey
• Multi-Task Learning as Multi-Objective Optimization - slide
• Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
• Personalized Approximate Pareto-Efficient Recommendation
• Pareto Multi-Task Learning
• SNR - Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
• Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
• Why I like it - multi-task learning for recommendation and explanation

• Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
• Pretraining Representations of Multi-modal Multi-query E-commerce Search

• [2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network
• [2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction
• APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction
• AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
• A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions
• Automatic Expert Selection for Multi-Scenario and Multi-Task Search
• Cross-Domain Recommendation - An Embedding and Mapping Approach
• Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
• Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
• Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space
• KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
• Leaving No One Behind- A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling
• Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
• Personalized Transfer of User Preferences for Cross-domain Recommendation
• SAR-Net - A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
• Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
• Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation

• [2019][Huawei][PAL] a position-bias aware learning framework for CTR prediction in live recommender systems
• AutoDebias - Learning to Debias for Recommendation
• Bias and Debias in Recommender System A Survey and Future Directions
• Deep Position-wise Interaction Network for CTR Prediction
• Denoising Implicit Feedback for Recommendation
• DVR - Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
• Improving Micro-video Recommendation by Controlling Position Bias
• Learning to rank with selection bias in personal search
• Unbiased Learning-to-Rank with Biased Feedback

• Attended Temperature Scaling - A Practical Approach for Calibrating Deep Neural Networks
• Beyond temperature scaling - Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
• Beta calibration - a well-founded and easily implemented improvement on logistic calibration for binary classifiers
• CALIBRATION OF NEURAL NETWORKS USING SPLINES
• Calibrated Recommendations
• Calibrating User Response Predictions in Online Advertising
• Crank up the volume - preference bias amplificationin collaborative recommendation
• Distribution-free calibration guarantees for histogram binning without sample splitting
• Field-aware Calibration - A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
• Mitigating Bias in Calibration Error Estimation
• Measuring Calibration in Deep Learning
• MBCT - Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
• On Calibration of Modern Neural Networks
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
• Obtaining Well Calibrated Probabilities Using Bayesian Binning
• Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
• Posterior Probability Matters - Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
• Transforming Classifier Scores into Accurate Multiclass Probability Estimates

• [2021][Tencent][DMTL] Distillation based Multi-task Learning - A Candidate Generation Model for Improving Reading Duration
• Ensembled CTR Prediction via Knowledge Distillation
• Privileged Features Distillation at Taobao Recommendations
• Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net
• Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System

• [2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling
• Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
• An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
• Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
• A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
• A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
• Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback
• Capturing Delayed Feedback in Conversion Rate Predictionvia Elapsed-Time Sampling
• Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions
• Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
• Dual Learning Algorithm for Delayed Conversions
• Handling many conversions per click in modeling delayed feedback
• Modeling Delayed Feedback in Display Advertising

• An Empirical Study of Training Self-Supervised Vision Transformers
• A Simple Framework for Contrastive Learning of Visual Representations
• Bootstrap Your Own Latent A New Approach to Self-Supervised Learning
• Contrastive Learning for Interactive Recommendation in Fashion
• Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
• CCL4Rec - Contrast over Contrastive Learning for Micro-video Recommendation
• Disentangled Contrastive Learning for Social Recommendation
• Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
• Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
• Improved Baselines with Momentum Contrastive Learning
• Multi-view Multi-behavior Contrastive Learning in Recommendation
• Momentum Contrast for Unsupervised Visual Representation Learning
• Multi-level Contrastive Learning Framework for Sequential Recommendation
• Predictive and Contrastive- Dual-Auxiliary Learning for Recommendation
• Understanding the Behaviour of Contrastive Loss
• Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

• [2017][MAML]Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• [2017][DropoutNet] DropoutNet - Addressing Cold Start in Recommender Systems
• [2017][HIN] Heterogeneous Information Network Embedding for Recommendation
• [2020][Wechat][ICAN] Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation
• [2021][Kuaishou][POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems
• Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
• Addressing the Item Cold-start Problem by Attribute-driven Active Learning
• A Practical Exploration System for Search Advertising
• A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
• Cold-start Sequential Recommendation via Meta Learner
• GIFT - Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
• Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering
• Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
• LHRM - A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform
• MAMO - Memory-Augmented Meta-Optimization for Cold-start Recommendation
• Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation
• Telepath - Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
• Warm Up Cold-start Advertisements - Improving CTR Predictions via Learning to Learn ID Embeddings

• An Empirical Evaluation of Thompson Sampling
• Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
• A Contextual-Bandit Approach to Personalized News Article Recommendation
• Comparison-based Conversational Recommender System with Relative Bandit Feedback

• A Meta-Learning Perspective on Cold-Start Recommendations for Items
• Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
• Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation
• MeLU - Meta-Learned User Preference Estimator for Cold-Start Recommendation
• Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
• Preference-Adaptive Meta-Learning for Cold-Start Recommendation
• Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
• Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

• LambdaRank - Learning to Rank with Nonsmooth Cost Functions
• RankNET - Learning to Rank Using Gradient Descent
• RankBoost - An Effcient Boosting Algorithm for Combining Preferences

• Learning to Rank Using Classification and Gradient

• AdaRank - A Boosting Algorithm for Information Retrieval
• From RankNet to LambdaRank to LambdaMART
• LambdaMART - Adapting Boosting for Information Retrieval Measures
• ListNet - Learning to Rank - From Pairwise Approach to Listwise Approach

• [2020][FairCo] Controlling Fairness and Bias in Dynamic Learning-to-Rank

• [2019][Tencent][RALM] Real-time Attention Based Look-alike Model for Recommender System
• A Sub-linear, Massive-scale Look-alike Audience Extension System
• Audience Expansion for Online Social Network Advertising
• Effective Audience Extension in Online Advertising
• Finding Users Who Act Alike - Transfer Learning for Expanding

• CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation
• Counterfactual Data-Augmented Sequential Recommendation
• Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue
• Causal Inference in Recommender Systems - A Survey and Future Directions
• Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
• Deconfounded Recommendation for Alleviating Bias Amplification
• Improving Ad Click Prediction by Considering Non-displayed Events
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
• Practical Counterfactual Policy Learning for Top-K Recommendations
• Recommendations as Treatments - Debiasing Learning and Evaluation

• [2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems
• A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks
• Adaptive, Personalized Diversity for Visual Discovery
• Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective
• DGCN - Diversified Recommendation with Graph Convolutional Networks
• Diversifying Search Results
• Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs
• Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation
• Exploiting Query Reformulations for Web Search Result Diversification
• Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
• Future-Aware Diverse Trends Framework for Recommendation
• Improving Recommendation Lists Through Topic Diversification
• Managing Diversity in Airbnb Search
• Novelty and Diversity in Information Retrieval Evaluation
• P-Companion - A Principled Framework for Diversified Complementary Product Recommendation
• UNDERSTANDING DIVERSITY IN SESSION-BASED RECOMMENDATION

• Overlapping Experiment Infrastructure - More, Better, Faster Experimentation

• Jointly Learning to Recommend and Advertise
• Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology
• Top-K Off-Policy Correctionfor a REINFORCE Recommender System