RecSysPapers

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。

联系方式

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
• [Effici

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