README_EN.md

Summary of Papers Related to Recommendation System

Introduce

1. Up to 2024-11-20, 873 papers related to recommendation system have been collected and summarized in this repo, including: Match, Pre-Rank, Rank, Re-Rank, Multi-Task, Multi-Scenario, Multi-Modal, Cold-Start, Calibration, Debias, Diversity, Fairness, Feedback-Delay, Distillation, Contrastive Learning, Casual Inference, Look-Alike, Learning-to-Rank, Reinforcement Learning and other fields, the repo will track the industry progress and update continuely.
2. Due to the restriction of special characters in the file name, all : in the title of the paper are changed to -. Bring to attention please when searching.
3. If the prefix of the file name contains [], it indicates that I have read it thoroughly. The first [] refers to the publication year of the paper, the second [] refers to the institution or company (optional), and the third [] refers to the abbreviation of the model or the method proposed in the paper (optional).
4. Below some of the primary categories, there are several secondary categories; A paper may involve multiple secondary categories (e.g., Match by Contrastive Learning), and eventually I will put the paper in the main category. The classification will be adjusted and optimized at any time, welcome to put forward any opinions in the issue.
5. If you are the author of the article and do not want your paper to exhibit here, please mention it in the issue. I will remove it immediately after verification.
6. About some Rank Algorithm implementation, please see another repo of mine: https://github.com/tangxyw/RecAlgorithm.
7. This repo is for exchange and study only, without any commercial purpose.

WeChat

Catalogue

• 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
• 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 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
• Enhancing CTR Prediction through Sequential Recommendation Pre-training- Introducing the SRP4CTR Framework
• 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
• 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 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
• 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
• 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 fnding 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

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
• 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

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

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
• 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
• 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
• 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
• 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
• 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
• 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
• On Sampling Strategies for Neural Network-based Collaborative Filtering
• 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
• VIER - Visual Imagination Enhanced Retrieval in Sponsored Search
• Variational Autoencoders for Collaborative Filtering
• gSASRec - Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

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

Mulit-Interset

• [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

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
• 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
• 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
• Multi-objective Learning to Rank by Model Distillation
• 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
• Touch the Core - Exploring Task Dependence Among Hybrid Targets for Recommendation
• Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction
• Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
• Why I like it - multi-task learning for recommendation and explanation

Multi-Modal

• 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
• End-to-end training of Multimodal Model and ranking Model
• 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
• MMBee - Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion
• Pretraining Representations of Multi-modal Multi-query E-commerce Search
• QARM - Quantitative Alignment Multi-Modal Recommendation at Kuaishou
• Universal Multi-modal Multi-domain Pre-trained Recommendation
• Unsupervised Multi-Modal Representation Learning for High Quality Retrieval of Similar Products at E-commerce Scale

Multi-Scenario

• [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 Conditional Expert Selection Network for Multi-domain Recommendation
• 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
• Cross-Domain Sequential Recommendation via Neural Process
• 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
• DIIT - A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
• EXIT - An EXplicit Interest Transfer Framework 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
• Moment&Cross - Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou
• 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

Debias

• [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

Calibration

• Attended Temperature Scaling - A Practical Approach for Calibrating Deep Neural Networks
• A Self-boosted Framework for Calibrated Ranking
• 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
• Deep Ensemble Shape Calibration - Multi-Field Post-hoc Calibration in Online Advertising
• 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

Distillation

• [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

Feedback-Delay

• [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

ContrastiveLearning

• 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

Cold-Start

• [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
• A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
• 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
• Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning
• SimRec - Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity
• 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
• Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
• Warm Up Cold-start Advertisements - Improving CTR Predictions via Learning to Learn ID Embeddings

Exploration&Exploitation

• 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

MetaLearning

• 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

Learning-to-Rank

• AliExpress Learning-To-Rank- Maximizing Online Model Performance without Going Online
• Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

Pair-wise

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

Point-wise

• Learning to Rank Using Classification and Gradient

List-wise

• 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

Fairness

• [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

Look-Alike

• [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

CausalInference

• 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
• Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation
• 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
• Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth
• 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
• Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
• 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
• Treatment Effect Estimation for User Interest Exploration on Recommender Systems

Diversity

• [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
• Contextual Distillation Model for Diversified Recommendation
• 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

ABTest

• All about Sample-Size Calculations for A:B Testing - Novel Extensions & Practical Guide
• Overlapping Experiment Infrastructure - More, Better, Faster Experimentation

ReinforcementLearning

• 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