- 截至2023-12-30，本仓库收集汇总了推荐系统领域相关论文共827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
- ReinforcementLearning
- [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
- DCN V2 - Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
- 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][JD] Micro Behaviors - A New Perspective in E-commerce Recommender Systems
- [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][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][Weibo][MaskNet] MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
- [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
- [2022][Alibaba] Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
- [2022][Google] On the Factory Floor - ML Engineering for Industrial-Scale Ads Recommendation Models
- [2023][Huawei] Ten Challenges in Industrial Recommender Systems
- [2023][Alibaba][JRC] Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
- [2023] Methodologies for Improving Modern Industrial Recommender Systems
- A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
- AutoSeqRec - Autoencoder for Efficient Sequential Recommendation
- Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
- Alternating Pointwise-Pairwise Learning for Personalized Item Ranking
- Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
- Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
- A Large Scale Prediction Engine for App Install Clicks and Conversions
- AutoMLP - Automated MLP for Sequential Recommendations
- A Deep Behavior Path Matching Network for Click-Through Rate Prediction
- Attention Mixtures for Time-Aware Sequential Recommendation
- A Self-Correcting Sequential Recommender
- Breaking the Curse of Quality Saturation with User-Centric Ranking
- Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
- Category-Specific CNN for Visual-aware CTR Prediction at JD.com
- ConsRec - Learning Consensus Behind Interactions for Group Recommendation
- CSPM - A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services
- 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
- Decision-Making Context Interaction Network for Click-Through Rate Prediction
- 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
- Deep Context Interest Network for Click-Through Rate Prediction
- Disentangling Long and Short-Term Interests for Recommendation
- E-Commerce Item Recommendation Based on Field-aware Factorization Machine
- Enhancing E-commerce Product Search through Reinforcement Learning-Powered Query Reformulation
- EXTR - Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search
- End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model
- Entire Space Learning Framework- Unbias Conversion Rate Prediction in Full Stages of Recommender System
- 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++ - Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
- Fragment and Integrate Network (FIN) - A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
- FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction
- FinalMLP - An Enhanced Two-Stream MLP Model for CTR Prediction
- GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction
- Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
- Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
- Generative Flow Network for Listwise Recommendation
- Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
- Hybrid Interest Modeling for Long-tailed Users
- Hierarchical Gating Networks for Sequential Recommendation
- HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction
- Inverse Learning with Extremely Sparse Feedback for Recommendation
- Improving Pairwise Learning for Item Recommendation from Implicit Feedback
- Improving Recommendation Quality in Google Drive
- Incorporating Social-aware User Preference for Video Recommendation
- Improving Deep Learning For Airbnb Search
- Interpretable User Retention Modeling in Recommendation
- Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
- Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
- Learning from All Sides - Diversified Positive Augmentation via Self-distillation in Recommendation
- Leveraging Watch-time Feedback for Short-Video Recommendations - A Causal Labeling Framework
- Long Short-Term Temporal Meta-learning in Online Recommendation
- Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
- 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
- Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
- Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
- Making Users Indistinguishable - Attribute-wise Unlearning in Recommender Systems
- Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
- MemoNet - Memorizing All Cross Features’ Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
- Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
- Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks
- MRIF - Multi-resolution Interest Fusion for Recommendation
- Micro-Behavior Encoding for Session-based Recommendation
- Neural News Recommendation with Negative Feedback
- News Recommendation with Candidate-aware User Modeling
- Out of the Box Thinking - Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection
- Optimizing Feature Set for Click-Through Rate Prediction
- Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
- PURS - Personalized Unexpected Recommender System for Improving User Satisfaction
- Query-dominant User Interest Network for Large-Scale Search Ranking
- Recommender Transformers with Behavior Pathways
- RUEL - Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
- Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
- Reweighting Clicks with Dwell Time in Recommendation
- Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
- Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
- Surrogate for Long-Term User Experience in Recommender Systems
- Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
- TrendSpotter - Forecasting E-commerce Product Trends
- Triangle Graph Interest Network for Click-through Rate Prediction
- To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders
- TencentRec - Real-time Stream Recommendation in Practice
- Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
- Temporal Interest Network for Click-Through Rate Prediction
- TransAct - Transformer-based Realtime User Action Model for Recommendation at Pinterest
- TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
- TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors
- User Behavior Retrieval for Click-Through Rate Prediction
- Visualizing and Understanding Deep Neural Networks in CTR Prediction
- Variance Reduction Using In-Experiment Data- Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes
- [2021][Tencent][R3S] Real-time Relevant Recommendation Suggestion
- [2022][Alibaba][DIAN] Deep Intention-Aware Network for Click-Through Rate Prediction
- [2022][Alibaba][DIHN] Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
- DPAN - Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
- [2010] RECON - A Reciprocal Recommender for Online Dating
- [2019] Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender Systems
- [2022] MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING
- [2022][Boss][DPGNN] Modeling Two-Way Selection Preference for Person-Job Fit
- BOSS - A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment
- Optimally Balancing Receiver and Recommended Users’ Importance in Reciprocal Recommender Systems
- Providing Explanations for Recommendations in Reciprocal Environments
- Reciprocal Recommendation for Job Matching with Bidirectional Feedback
- Reciprocal Recommendation System for Online Dating
- Reciprocal Sequential Recommendation
- Reciprocal Recommendation Algorithm for the Field of Recruitment
- Supporting users in fnding successful matches in reciprocal recommender systems
- A Content-Driven Micro-Video Recommendation Dataset at Scale
- KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
- KuaiSAR - A Unified Search And Recommendation Dataset
- KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems
- MobileRec - A Large-Scale Dataset for Mobile Apps Recommendation
- REASONER - An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths
- Tenrec - A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
- U-NEED - A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
- [2019][Bytedance] What You Look Matters? Offline Evaluation of Advertising Creatives for Cold-start Problem
- [2021][Baidu][GemNN] GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction
- Automated Creative Optimization for E-Commerce Advertising
- A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising
- CREATER - CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
- Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
- Learning to Create Better Ads- Generation and Ranking Approaches for Ad Creative Refinement
- [2020][Tencent][DFN] Deep Feedback Network for Recommendation
- [2021][Tencent][CDR] Curriculum Disentangled Recommendation with Noisy Multi-feedback
- Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
- Capturing Conversion Rate Fluctuation during Sales Promotions - A Novel Historical Data Reuse Approach
- Multi-task based Sales Predictions for Online Promotions
- Bundle Recommendation with Graph Convolutional Networks
- Bundle MCR - Towards Conversational Bundle Recommendation
- CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation
- Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
- POG - Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
- AutoField - Automating Feature Selection in Deep Recommender Systems
- MvFS - Multi-view Feature Selection for Recommender System
- SHARK - A Lightweight Model Compression Approach for Large-scale Recommender Systems
- [2022][Tencent][NoveNet] Modeling User Repeat Consumption Behavior for Online Novel Recommendation
- Buy It Again - Modeling Repeat Purchase Recommendations
- Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems
- On the Value of Reminders within E-Commerce Recommendations
- Predicting Consumption Patterns with Repeated and Novel Events
- Predicting Music Relistening Behavior Using the ACT-R Framework
- Personalized Category Frequency prediction for Buy It Again recommendations
- Recommendation on Live-Streaming Platforms - Dynamic Availability and Repeat Consumption
- RepeatNet - A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
- Recommendation for Repeat Consumption from User Implicit Feedback
- The Dynamics of Repeat Consumption
- Will You “Reconsume” the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors
- [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 Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
- A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
- Empowering Next POI Recommendation with Multi-Relational Modeling
- Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation
- GETNext - Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
- Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
- Location Embeddings for Next Trip 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
- ODNET - A Novel Personalized Origin-Destination Ranking Network for Flight Recommendation
- 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
- STGIN - Spatial-Temporal Graph Interaction Network for Large-scale 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
- When Online Meets Offline - Exploring Periodicity for Travel Destination Prediction
- Where to Go Next - A Spatio-Temporal Gated Network for Next POI 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
- Learning to Personalize Recommendations based on Customers’ Shopping Intents
- Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
- NEON - Living Needs Prediction System in Meituan
- [2020][Tencent][AETN] General-Purpose User Embeddings based on Mobile App Usage
- [2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest
- Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
- [2020][Twitter] Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
- [2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation
- AutoEmb - Automated Embedding Dimensionality Search in Streaming Recommendations
- Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
- Binary Code based Hash Embedding for Web-scale Applications
- Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
- Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
- 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
- ADER - Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
- A Survey on Incremental Update for Neural Recommender Systems
- [2014][Yahoo] Beyond Clicks - Dwell Time for Personalization
- Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
- Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
- A hybrid two-stage recommender system for automatic playlist continuation
- A Line in the Sandv- Recommendation or Ad-hoc Retrieval
- Automatic Playlist Continuation using Subprofile-Aware Diversification
- A Hybrid Recommender System for Improving Automatic Playlist Continuation
- Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation
- Automatic playlist continuation using a hybrid recommender system combining features from text and audio
- An Ensemble Approach of Recurrent Neural Networks using Pre-Trained Embeddings for Playlist Completion
- An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation
- Automatic Music Playlist Continuation via Neighbor-based Collaborative Filtering and Discriminative Reweighting:Reranking
- Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario
- Consistency-Aware Recommendation for User-Generated ItemList Continuation
- Dual-interest Factorization-heads Attention for Sequential Recommendation
- Efficient Similarity Based Methods For The Playlist Continuation Task
- Efficient K-NN for Playlist Continuation
- Effective Nearest-Neighbor Music Recommendations
- MMCF - Multimodal Collaborative Filtering for Automatic Playlist Continuation
- MUSE - Music Recommender System with Shuffle Play Recommendation Enhancement
- Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms
- Random Walk with Restart for Automatic Playlist Continuation and Query-Specific Adaptations
- Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist Continuation
- TrailMix - An Ensemble Recommender System for Playlist Curation and Continuation
- Towards Seed-Free Music Playlist Generation
- Two-stage Model for Automatic Playlist Continuation at Scale
- Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation
- User Recommendation in Content Curation Platforms
- [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
- [2022][Meituan] Contrastive Information Transfer for Pre-Ranking Systems
- AutoFAS - Automatic Feature and Architecture Selection for Pre-Ranking System
- COPR - Consistency-Oriented Pre-Ranking for Online Advertising
- Cascade Ranking for Operational E-commerce Search
- EENMF - An End-to-End Neural Matching Framework for E-Commerce Sponsored Search
- IntTower - the Next Generation of Two-Tower Model for Pre-Ranking System
- Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
- [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
- Controllable Multi-Objective Re-ranking with Policy Hypernetworks
- Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems
- Discrete Conditional Diffusion for Reranking in Recommendation
- DEAR - Deep Reinforcement Learning for Online Advertising Impression in 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
- Multi-channel Integrated Recommendation with Exposure Constraints
- Neural Re-ranking in Multi-stage Recommender Systems - A Review
- Practical Diversified Recommendations on YouTube with Determinantal Point Processes
- PIER - Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
- 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
- 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][Baidu] Sample Optimization For Display Advertising
- [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
- [2021][Alibaba][XDM] XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
- [2023] Adap-tau - Adaptively Modulating Embedding Magnitude for Recommendation
- [2023][JD][MMSE] Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce 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
- An Empirical Study of Selection Bias in Pinterest Ads Retrieval
- AutoRec - Autoencoders Meet Collaborative Filtering
- A Simple Convolutional Generative Network for Next Item Recommendation
- A Dual Augmented Two-tower Model for Online Large-scale Recommendation
- Binary Embedding-based Retrieval at Tencent
- Beyond Two-Tower Matching - Learning Sparse Retrievable Cross-Interactions for Recommendation
- Build Faster with Less - A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search
- Beyond Semantics - Learning a Behavior Augmented Relevance Model with Self-supervised Learning
- 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
- Divide and Conquer - Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective
- Disentangled Self-Supervision in Sequential Recommenders
- Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
- Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation
- 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
- Fast Matrix Factorization for Online Recommendation with Implicit Feedback
- 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
- I^3 Retriever- Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
- 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 from History and Present - Next-item Recommendation via Discriminatively Exploiting User Behaviors
- Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
- Locker - Locally Constrained Self-Attentive Sequential Recommendation
- Multi-Objective Personalized Product Retrieval in Taobao Search
- Modeling Dynamic Missingness of Implicit Feedback for Recommendation
- MV-HAN - A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
- Neighborhood-based Hard Negative Mining for Sequential Recommendation
- NAIS - Neural Attentive Item Similarity Model for Recommendation
- Neural A�entive Session-based Recommendation
- Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates
- Outer Product-based Neural Collaborative Filtering
- On the Theories Behind Hard Negative Sampling for Recommendation
- PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest
- Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
- Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
- Path-based Deep Network for Candidate Item Matching in Recommenders
- Que2Search - Fast and Accurate Query and Document Understanding for Search at Facebook
- Que2Engage - Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace
- Recommender Systems with Generative Retrieval
- Representing and Recommending Shopping Baskets with Complementarity, Compatibility, and Loyalty
- Robust Representation Learning for Unified Online Top-K Recommendation
- Revisiting Neural Retrieval on Accelerators
- Recommendation on Live - Streaming Platforms- Dynamic Availability and Repeat Consumption
- Sequential Recommender System based on Hierarchical Attention Network
- Sequential Recommendation via Stochastic Self-Attention
- Semi-supervised Adversarial Learning for Complementary Item Recommendation
- Sparse-Interest Network for Sequential Recommendation
- Self-Attentive Sequential Recommendation
- StarSpace - Embed All The Things!
- SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
- SimpleX - A Simple and Strong Baseline for Collaborative Filtering
- Towards Automated Negative Sampling in Implicit Recommendation
- Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning
- Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search
- Uni-Retriever - Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
- Variational Autoencoders for Collaborative Filtering
- gSASRec - Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
- 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
- Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
- Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
- Collaborative Filtering Recommender Systems
- GroupLens - An open architecture for collaborative filtering of Netnews
- Item-Based Collaborative Filtering Recommendation Algorithms
- MatchSim - a novel similarity measure based on maximum neighborhood matching
- [2019][Alibaba][MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
- [2020][Alibaba][ComiRec] Controllable Multi-Interest Framework for Recommendation
- Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation
- Density Weighting for Multi-Interest Personalized 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
- A Survey of Graph Neural Networks for Social Recommender Systems
- Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
- Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
- Compressed Interaction Graph based Framework for Multi-behavior Recommendation
- 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
- Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation
- E-commerce Search via Content Collaborative Graph Neural Network
- 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
- Hessian-aware Quantized Node Embeddings for Recommendation
- Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
- IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
- LightSAGE - Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation
- MultiSage - Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks
- Modeling Dual Period-Varying Preferences for Takeaway Recommendation
- MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
- MultiBiSage - A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
- 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
- Spherical Graph Embedding for Item Retrieval in Recommendation System
- SimClusters - Community-Based Representations for Heterogeneous Recommendations at Twitter
- Self-Supervised Hypergraph Transformer for Recommender Systems
- TwHIN - Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
- Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
- 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
- A CLOSER LOOK AT LOSS WEIGHTING IN MULTI-TASK LEARNING
- AdaTask - A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
- AdaTT - Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
- Can Small Heads Help Understanding and Improving Multi-Task Generalization
- Cross-stitch Networks for Multi-task Learning
- Conflict-Averse Gradient Descent for Multi-task Learning
- CAM2 - Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems
- Dynamic Task Prioritization for Multitask Learning
- Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
- Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
- 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
- Feature Decomposition for Reducing Negative Transfer - A Novel Multi-task Learning Method for Recommender System
- Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
- HyperGrid Transformers - Towards A Single Model for Multiple Tasks
- Improving Training Stability for Multitask Ranking Models in Recommender Systems
- 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
- Multitask Ranking System for Immersive Feed and No More Clicks - A Case Study of Short-Form Video Recommendation
- 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
- Multi-Task Deep Recommender Systems - A Survey
- NCS4CVR - Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction
- Optimizing Airbnb Search Journey with Multi-task Learning
- Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks
- Personalized Approximate Pareto-Efficient Recommendation
- Pareto Multi-Task Learning
- Single-shot Feature Selection for Multi-task Recommendations
- STEM - Unleashing the Power of Embeddings for Multi-task Recommendation
- STAN - Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
- 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
- Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
- Bootstrap Latent Representations for Multi-modal Recommendation
- ContentCTR - Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer
- COURIER - Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features
- Heterogeneous Attention Network for Effective and Efficient Cross-modal Retrieval
- Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieva
- Multimodal Recommender Systems - A Survey
- Multi-Modal Self-Supervised Learning for Recommendation
- MM-GEF - Multi-modal representation meet collaborative filtering
- Pretraining Representations of Multi-modal Multi-query E-commerce Search
- Universal Multi-modal Multi-domain Pre-trained Recommendation
- Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale
- [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
- [2021][Baidu] Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising
- [2022][AntGroup][AESM2] Automatic Expert Selection for Multi-Scenario and Multi-Task Search
- [2023][Meituan][HiNet] HiNet - Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
- [2023][Kuaishou][PEPNet] PEPNet - Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
- A Deep Framework for Cross-Domain and Cross-System Recommendations
- APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction
- ADL - Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction
- AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
- A Collaborative Transfer Learning Framework for Cross-domain Recommendation
- A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions
- Adaptive Domain Interest Network for Multi-domain Recommendation
- BOMGraph - Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network
- Cross-domain recommendation via user interest alignment
- Cross-Domain Recommendation- Challenges, Progress, and Prospects
- Cross-domain Augmentation Networks for Click-Through Rate Prediction
- Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks
- CDR-Adapter - Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
- Cross-Domain Recommendation - An Embedding and Mapping Approach
- CoNet - Collaborative Cross Networks for Cross-Domain Recommendation
- Cross domain recommendation based on multi-type media fusion
- Cross-domain Recommendation Without Sharing User-relevant Data
- Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
- Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
- 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
- DDTCDR - Deep Dual Transfer Cross Domain Recommendation
- DeepAPF - Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation
- DisenCDR - Learning Disentangled Representations for Cross-Domain Recommendation
- Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks
- Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
- HAMUR - Hyper Adapter for Multi-Domain 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
- Mixed Attention Network for Cross-domain Sequential Recommendation
- Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
- Multi-Scenario Ranking with Adaptive Feature Learning
- Personalized Transfer of User Preferences for Cross-domain Recommendation
- Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
- SAMD - An Industrial Framework for Heterogeneous Multi-Scenario Recommendation
- SAR-Net - A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
- Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction
- Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
- 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
- [2020][Alibaba][ESAM] ESAM - Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
- Are You Influenced by Others When Rating? Improve Rating Prediction by Conformity Modeling
- A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data
- AutoDebias - Learning to Debias for Recommendation
- Bias and Debias in Recommender System - A Survey and Future Directions
- Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders
- Counterfactual Video Recommendation for Duration Debiasing
- Causal Intervention for Leveraging Popularity Bias in Recommendation
- Deep Position-wise Interaction Network for CTR Prediction
- Debiased Recommendation with User Feature Balancing
- Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering
- Denoising Implicit Feedback for Recommendation
- DVR - Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
- Deconvolving Feedback Loops in Recommender Systems
- Disentangling User Interest and Conformity for Recommendation with Causal Embedding
- Degenerate Feedback Loops in Recommender Systems
- Influence Function for Unbiased Recommendation
- Improving Ad Click Prediction by Considering Non-displayed Events
- Improving Micro-video Recommendation by Controlling Position Bias
- Learning to rank with selection bias in personal search
- Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
- Recommendations as Treatments - Debiasing Learning and Evaluation
- Rec4Ad - A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
- Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems
- Training and Testing of Recommender Systems on Data Missing Not at Random
- UKD - Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
- Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
- 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
- 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
- Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
- 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
- Unbiased Knowledge Distillation for Recommendation
- [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
- A Multi-Task Learning Approach for Delayed Feedback Modeling
- 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
- Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
- Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
- Handling many conversions per click in modeling delayed feedback
- Modeling Delayed Feedback in Display Advertising
- Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
- 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 Conversion Rate Prediction
- Contrastive Learning for Interactive Recommendation in Fashion
- CL4CTR - A Contrastive Learning Framework for CTR Prediction
- Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
- Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
- CCL4Rec - Contrast over Contrastive Learning for Micro-video Recommendation
- Disentangled Causal Embedding With Contrastive Learning For Recommender System
- 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
- [2019][Microsoft][CB2CF] CB2CF - A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations
- [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
- An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
- Addressing the Item Cold-start Problem by Attribute-driven Active Learning
- A Practical Exploration System for Search Advertising
- A Model of Two Tales - Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
- A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
- Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching
- Contrastive Learning for Cold-Start Recommendation
- Cold-start Sequential Recommendation via Meta Learner
- Contrastive Collaborative Filtering for Cold-Start Item Recommendation
- Cold & Warm Net - Addressing Cold-Start Users in Recommender Systems
- Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
- Fresh Content Needs More Attention - Multi-funnel Fresh Content Recommendation
- GIFT - Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
- Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering
- Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
- Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
- Long-tail Augmented Graph Contrastive Learning for Recommendation
- Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
- Long-Tail Learning via Logit Adjustment
- 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
- Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models
- SMINet - State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation
- Task-adaptive Neural Process for User 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
- Value of Exploration - Measurements, Findings and Algorithms
- 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
- Blocked Collaborative Bandits - Online Collaborative Filtering with Per-Item Budget Constraints
- Comparison-based Conversational Recommender System with Relative Bandit Feedback
- Efficient Sparse Linear Bandits under High Dimensional Data
- Scalable Neural Contextual Bandit for Recommender Systems
- 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
- AliExpress Learning-To-Rank- Maximizing Online Model Performance without Going Online
- Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
- 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
- RankFormer - Listwise Learning-to-Rank Using Listwide Labels
- [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
- Implicit Look-alike Modelling in Display Ads - Transfer Collaborative Filtering to CTR Estimation
- 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
- A Counterfactual Collaborative Session-based Recommender System
- A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
- Addressing Confounding Feature Issue for Causal Recommendation
- CausCF - Causal Collaborative Filtering for Recommendation Effect Estimation
- Causal Inference in Recommender Systems - A Survey of Strategies for Bias Mitigation, Explanation, and Generalization
- 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
- Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
- Mitigating Hidden Confounding Effects for Causal Recommendation
- On the Opportunity of Causal Learning in Recommendation Systems - Foundation, Estimation, Prediction and Challenges
- Practical Counterfactual Policy Learning for Top-K Recommendations
- Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability
- Towards Unbiased and Robust Causal Ranking for Recommender Systems
- Top-N Recommendation with Counterfactual User Preference Simulation
- [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
- A Survey of Diversification Techniques in Search and Recommendation
- Adaptive, Personalized Diversity for Visual Discovery
- Calibrated Recommendations
- Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective
- DGCN - Diversified Recommendation with Graph Convolutional Networks
- DGRec - Graph Neural Network for Recommendation with Diversified Embedding Generation
- 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
- Graph Exploration Matters- Improving both individual-level and system-level diversity in WeChat Feed Recommender
- Improving Recommendation Lists Through Topic Diversification
- Learning To Rank Diversely At Airbnb
- Multi-factor Sequential Re-ranking with Perception-Aware Diversification
- Managing Diversity in Airbnb Search
- Novelty and Diversity in Information Retrieval Evaluation
- P-Companion - A Principled Framework for Diversified Complementary Product Recommendation
- Sliding Spectrum Decomposition for Diversified Recommendation
- User-controllable Recommendation Against Filter Bubbles
- UNDERSTANDING DIVERSITY IN SESSION-BASED RECOMMENDATION
- All about Sample-Size Calculations for A:B Testing - Novel Extensions & Practical Guide
- Overlapping Experiment Infrastructure - More, Better, Faster Experimentation
- A Reinforcement Learning Framework for Explainable Recommendation
- Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
- DRN - A Deep Reinforcement Learning Framework for News Recommendation
- Deep Reinforcement Learning for List-wise Recommendations
- Deep Reinforcement Learning for Search, Recommendation, and Online Advertising - A Survey
- Deep Reinforcement Learning for Page-wise Recommendations
- Exploration and Regularization of the Latent Action Space in Recommendation
- InTune - Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
- Jointly Learning to Recommend and Advertise
- Large-scale Interactive Recommendation with Tree-structured Policy Gradient
- Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
- Off-policy evaluation for slate recommendation
- Online Matching - A Real-time Bandit System for Large-scale Recommendations
- Reinforcing User Retention in a Billion Scale Short Video Recommender System
- Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
- Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology
- Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation
- Top-K Off-Policy Correctionfor a REINFORCE Recommender System
- Two-Stage Constrained Actor-Critic for Short Video Recommendation
- Towards Capacity-Aware Broker Matching - From Recommendation to Assignment
- Virtual-Taobao - Virtualizing Real-world Online Retail Environment for Reinforcement Learning
- When People Change their Mind - Off-Policy Evaluation in Non-stationary Recommendation Environments