推荐系统相关论文汇总

(English Version is Here)

介绍

1. 截至2026-06-11，本仓库收集汇总了推荐系统领域相关论文共948篇，涉及：召回，粗排，精排，重排，多任务，多场景，多模态，冷启动，校准， 纠偏，多样性，公平性，反馈延迟，蒸馏，对比学习，因果推断，Look-Alike，Learning-to-Rank，强化学习等领域，本仓库会跟踪业界进展，持续更新。
2. 因文件名特殊字符的限制，故论文title中所有的:都改为了-，检索时请注意。
3. 文件名前缀中带有[]的，表明本人已经通读过，第一个[]中为论文年份，第二个[]中为发表机构或公司(可选)，第三个[]中为论文提出的model或method的简称(可选)。
4. 在某些一级分类下面，还有若干二级分类；一篇论文可能应该涉及多个二级分类(例如用对比学习的方法做召回)，最终我会将论文放在较主要的那一类下；分类也会随时调整优化，欢迎在issue中提出宝贵意见。
5. 若您是文章作者，且不希望您的论文出现在这里，请在issue中提出，我核实后会马上下架。
6. 关于排序算法的一些实现，请见我的另一个repo: https://github.com/tangxyw/RecAlgorithm
7. 本仓库仅供交流学习使用，不做任何商业目的。

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论文目录

• 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

Rank

• [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

Industry

• [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
• All-domain Moveline Evolution Network for Click-Through Rate Prediction
• 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
• Aligned Side Information Fusion Method for Sequential Recommendation
• Attention Mixtures for Time-Aware Sequential Recommendation
• A Self-Correcting Sequential Recommender
• Beyond Cascaded Architectures - An End-to-end Generative Framework for Industrial Advertising
• 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
• Deep Group Interest Network on Full Lifelong User Behaviors for CTR Prediction
• 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 Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction
• Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
• DV365 - Extremely Long User History Modeling at Instagram
• 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
• Enhancing CTR Prediction through Sequential Recommendation Pre-training- Introducing the SRP4CTR Framework
• Ensure Timeliness and Accuracy - A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation
• E-Commerce Item Recommendation Based on Field-aware Factorization Machine
• Enhancing E-commerce Product Search through Reinforcement Learning-Powered Query Reformulation
• Enhancing CTR prediction in Recommendation Domain with Search Query Representation
• 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
• EGA-V1 - Unifying Online Advertising with End-to-End Learning
• Efficient Long Sequential Low-rank Adaptive Attention for Click-through rate Prediction
• EGA-V2 - An End-to-end Generative Framework for Industrial Advertising
• 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
• Hesitation and Tolerance in Recommender Systems
• Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
• Hybrid Interest Modeling for Long-tailed Users
• HyFormer - Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction
• 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 Sequential Recommender Systems with Online and In-store User Behavior
• 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
• Improving Long-tail User CTR Prediction via Hierarchical Distribution Alignment
• Interpretable User Retention Modeling in Recommendation
• Improving feature interactions at Pinterest under industry constraints
• 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
• LONG-SEQUENCE RECOMMENDATION MODELS NEED DECOUPLED EMBEDDINGS
• Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
• Long-Term Interest Clock - Fine-Grained Time Perception in Streaming Recommendation System
• LambdaFM - Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
• LONGER - Scaling Up Long Sequence Modeling in Industrial Recommenders
• Learning Within-Session Budgets from Browsing Trajectories
• Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
• Modeling User Retention through Generative Flow Networks
• 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
• MARM - Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity
• Micro-Behavior Encoding for Session-based Recommendation
• Neural News Recommendation with Negative Feedback
• News Recommendation with Candidate-aware User Modeling
• 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
• TransAct V2 - Lifelong User Action Sequence Modeling on Pinterest Recommendation
• TWIN - TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
• TWIN V2 - Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced 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

TriggerInduced

• [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
• Click A, Buy B - Rethinking Conversion Attribution in ECommerce Recommendations
• Collaborative Contrastive Network for Click-Through Rate Prediction
• DPAN - Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
• Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
• Modeling User Intent Beyond Trigger - Incorporating Uncertainty for Trigger-Induced Recommendation

Reciprocal

• [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
• CUPID - A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform
• 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 finding successful matches in reciprocal recommender systems

Dataset

• 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
• RECFLOW - AN INDUSTRIAL FULL FLOW RECOMMENDATION DATASET
• 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

CreativeSelection

• [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

NegativeFeedback

• [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

BigPromotion

• Capturing Conversion Rate Fluctuation during Sales Promotions - A Novel Historical Data Reuse Approach
• Multi-task based Sales Predictions for Online Promotions
• TemporalExpertNet - Cross-Temporal Knowledge Reuse forPromotion-Aware CVR Prediction

LifetimeValue

• ADSNet - Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising
• Out of the Box Thinking - Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection
• OptDist - Learning Optimal Distribution for Customer Lifetime Value Prediction

Bundle

• 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

Edge

• Real-time Short Video Recommendation on Mobile Devices

FeatureSelection

• 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

RepeatConsumption

• [2022][Tencent][NoveNet] Modeling User Repeat Consumption Behavior for Online Novel Recommendation
• Buy It Again - Modeling Repeat Purchase Recommendations
• FIM - Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation
• 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

POI

• [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

Intent

• 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
• USD - A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations

Representation

• [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

FeatureHashing

• [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
• AdaS&S - a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System
• 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

Interactive

• Q&R - A Two-Stage Approach toward Interactive Recommendation

IncrementalLearning

• ADER - Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
• A Survey on Incremental Update for Neural Recommender Systems

Regression

• [2014][Yahoo] Beyond Clicks - Dwell Time for Personalization
• Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation
• CREAD - A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems
• Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
• Explicit Uncertainty Modeling for Video Watch Time Prediction
• TranSUN - A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
• Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

AutomaticPlaylistContinuation

• 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

Pre-Rank

• [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
• A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
• A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation 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
• HIT Model - A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems
• IntTower - the Next Generation of Two-Tower Model for Pre-Ranking System
• InteractRank - Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features
• Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System

Re-Rank

• [2018][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
• [2020][LinkedIn] Ads Allocation in Feed via Constrained Optimization
• Beyond Pairwise Learning-To-Rank At Airbnb
• 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
• Do Not Wait - Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
• From Generation to Consumption - Personalized List Value Estimation for Re-ranking
• 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
• Non-autoregressive Generative Models for Reranking Recommendation
• 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
• Towards Principled Learning for Re-ranking in Recommender Systems
• User Response Models to Improve a REINFORCE Recommender System

Match

• [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
• Bootstrapping Conditional Retrieval for User-to-Item Recommendations
• Beyond Semantics - Learning a Behavior Augmented Relevance Model with Self-supervised Learning
• Balancing Semantic Relevance and Engagement in Related Video Recommendations
• 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
• CSMF - Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval
• Collaborative Deep Learning for Recommender Systems
• CRM - Retrieval Model with Controllable Condition
• CroPS - Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search
• 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
• ESANS - Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems
• Efficient Training on Very Large Corpora via Gramian Estimation
• Extreme Multi-label Learning for Semantic Matching in Product Search
• Embedding-based Retrieval in Multimodal Content Moderation
• Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model
• Fast Matrix Factorization for Online Recommendation with Implicit Feedback
• HeterRec - Heterogeneous Information Transformer for Scalable Sequential Recommendation
• Hierarchical Structured Neural Network- Efficient Retrieval Scaling for Large Scale Recommendation
• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
• Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching
• Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning
• 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
• KuaiFormer - Transformer-Based Retrieval at Kuaishou
• 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
• LongRetriever - Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
• Multi-Objective Personalized Product Retrieval in Taobao Search
• M5 - Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
• Modeling Dynamic Missingness of Implicit Feedback for Recommendation
• MV-HAN - A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
• Neural Attentive Session-based Recommendation
• Neighborhood-based Hard Negative Mining for Sequential Recommendation
• NAIS - Neural Attentive Item Similarity Model for Recommendation
• Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates
• Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
• Outer Product-based Neural Collaborative Filtering
• On the Theories Behind Hard Negative Sampling for Recommendation
• On Sampling Strategies for Neural Network-based Collaborative Filtering
• PI2I - A Personalized Item-Based Collaborative Filtering Retrieval Framework
• 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
• Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever
• RADAR - Recall Augmentation through Deferred Asynchronous Retrieval
• Revisiting Neural Retrieval on Accelerators
• Retrieval with Learned Similarities
• 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
• Synergizing Implicit and Explicit User Interests - A Multi-Embedding Retrieval Framework at Pinterest
• Self-Attentive Sequential Recommendation
• StarSpace - Embed All The Things!
• SPM - Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
• Suggest, complement, inspire - story of Two Tower recommendations at Allegro.com
• 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
• User Long-Term Multi-Interest Retrieval Model for Recommendation
• Unifying Generative and Dense Retrieval for Sequential Recommendation
• VIER - Visual Imagination Enhanced Retrieval in Sponsored Search
• Variational Autoencoders for Collaborative Filtering
• gSASRec - Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

Mulit-Interest

• [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

Tree-Based

• 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

ANN

• 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

Nearline

• Truncation-Free Matching System for Display Advertising at Alibaba

Classic

• 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

GNN

• [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
• OmniSage - Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
• 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

Multi-Task

• [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
• DTN - Deep Multiple Task-specific Feature Interactions 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
• HoME - Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou
• HarmonRank - Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation
• HyperGrid Transformers - Towards A Single Model for Multiple Tasks
• Improving Training Stability for Multitask Ranking Models in Recommender Systems
• INFNet - A Task-aware Information Flow Network for Large-Scale Recommendation Systems
• Learning to Recommend with Multiple Cascading Behaviors
• [MetaBalance - Improving Multi-Task Rec