- 截至2022-11-25，本仓库收集汇总了推荐系统领域相关论文共486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。
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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
- Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
- Deep Learning Recommendation Model for Personalization and Recommendation System
- Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
- 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
- LambdaFM - Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
- Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
- Learning Within-Session Budgets from Browsing Trajectories
- 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
- Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
- User Behavior Retrieval for Click-Through Rate Prediction
- [2021][Tencent][R3S] Real-time Relevant Recommendation Suggestion
- [2022][Alibaba][DIHN] Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
- [2022][Boss][DPGNN] Modeling Two-Way Selection Preference for Person-Job Fit
- Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender Systems
- MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING
- Optimally balancing receiver and recommended users' importance in reciprocal recommender systems
- Reciprocal Recommendation for Job Matching with Bidirectional Feedback
- Reciprocal Recommendation System for Online Dating
- RECON - A Reciprocal Recommender for Online Dating
- Reciprocal Recommendation Algorithm for the Field of Recruitment
- Supporting users in fnding successful matches in reciprocal recommender systems
- KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
- KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems
- Tenrec - A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
- Bundle Recommendation with Graph Convolutional Networks
- CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation
- Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
- [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
- Personalized Long- and Short-term Preference Learning for Next POI 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
- [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
- Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
- Memory-efficient Embedding for Recommendations
- Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
- [2014][Yahoo] Beyond Clicks - Dwell Time for Personalization
- 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 Complementary Product 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
- [2015][Sceptre] Inferring Networks of Substitutable and Complementary Products
- [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
- Attentive Collaborative Filtering - Multimedia Recommendation with Item- and Component-Level A�ention
- Attentive Sequential Models of Latent Intent for Next Item Recommendation
- A User-Centered Concept Mining System for Query and Document Understanding at Tencent
- AutoRec - Autoencoders Meet Collaborative Filtering
- A Dual Augmented Two-tower Model for Online Large-scale Recommendation
- CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
- Coarse-to-Fine Sparse Sequential Recommendation
- Cross-Batch Negative Sampling for Training Two-Tower Recommenders
- Collaborative Deep Learning for Recommender Systems
- Deep Matrix Factorization Models for Recommender Systems
- Disentangled Self-Supervision in Sequential Recommenders
- Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
- 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
- Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
- Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
- Itinerary-aware Personalized Deep Matching at Fliggy
- Improving Recommendation Accuracy using Networks of Substitutable and Complementary Products
- ItemSage - Learning Product Embeddings for Shopping Recommendations at Pinterest
- Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
- Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
- Locker - Locally Constrained Self-Attentive Sequential Recommendation
- NAIS - Neural Attentive Item Similarity Model for Recommendation
- Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates
- Outer Product-based Neural Collaborative Filtering
- PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest
- Path-based Deep Network for Candidate Item Matching in Recommenders
- Representing and Recommending Shopping Baskets with Complementarity, Compatibility, and Loyalty
- Recommendation on Live - Streaming Platforms- Dynamic Availability and Repeat Consumption
- Sequential Recommendation via Stochastic Self-Attention
- Sparse-Interest Network for Sequential Recommendation
- Self-Attentive Sequential Recommendation
- StarSpace - Embed All The Things!
- 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
- Variational Autoencoders for Collaborative Filtering
- XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
- Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations
- Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
- Learning Optimal Tree Models under Beam Search
- Learning Tree-based Deep Model for Recommender Systems
- 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
- Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
- Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
- DC-GNN - Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval
- 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
- FASTGCN - FAST LEARNING WITH GRAPH CONVOLUTIONAL NETWORKS VIA IMPORTANCE SAMPLING
- 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
- Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
- IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
- MultiSage - Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks
- MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
- M2GRL - A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
- 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
- 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
- Dynamic Task Prioritization for Multitask Learning
- 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
- Learning to Recommend with Multiple Cascading Behaviors
- 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
- A Deep Framework for Cross-Domain and Cross-System Recommendations
- 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 via Bi-directional Transfer Graph Collaborative Filtering Networks
- Cross-Domain Recommendation - An Embedding and Mapping Approach
- CoNet - Collaborative Cross Networks for Cross-Domain Recommendation
- Cross-domain Recommendation Without Sharing User-relevant Data
- Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
- DTCDR - A Framework for Dual-Target Cross-Domain Recommendation
- Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
- DeepAPF - Deep Attentive Probabilistic Factorization for Multi-site Video 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
- Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users
- 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
- Disentangling User Interest and Conformity for Recommendation with Causal Embedding
- 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
- What You Look Matters Offline Evaluation of Advertising Creatives for Cold-start Problem
- 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
- 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
- Equity of Attention - Amortizing Individual Fairness in Rankings
- Fairness in Recommendation Ranking through Pairwise Comparisons
- [2019][Tencent][RALM] Real-time Attention Based Look-alike Model for Recommender System
- [2019][Pinterest] Finding Users Who Act Alike - Transfer Learning for Expanding
- A Sub-linear, Massive-scale Look-alike Audience Extension System
- Adversarial Factorization Autoencoder for Look-alike Modeling
- A Feature-Pair-based Associative Classification Approach to Look-alike Modeling for Conversion-Oriented User-Targeting in Tail Campaigns
- Audience Expansion for Online Social Network Advertising
- Comprehensive Audience Expansion based on End-to-End Neural Prediction
- Effective Audience Extension in Online Advertising
- Hubble - An Industrial System for Audience Expansion in Mobile Marketing
- Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
- Score Look-alike Audiences
- Two-Stage Audience Expansion for Financial Targeting in Marketing
- 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