Awesome Recsys

I share information related to the Recommender Systems that I am interested in. They consist of SIGIR, RecSys, ICLR, NeurIPS, ICML, AAAI, IJCAI, KDD, etc.

• modified: 2024-01-13

Conference Paper

SIGIR, Recsys, WSDM, KDD, etc.

None means unavailable URL or papers that have not been published yet.

• 2023

  • WSDM 2023
  • ICLR 2023
  • AAAI 2023
  • WWW 2023
  • Recsys 2023
  • SIGIR 2023
  • KDD 2023
  • CIKM 2023
  • IJCAI 2023
  • ICML 2023
  • NeurIPS'23

• 2022

  • SIGIR 2022
  • RecSys 2022
  • KDD 2022
  • WSDM 2022
  • CIKM 2022
  • WWW 2022
  • IJCAI 2022
  • ICML 2022

2023

WSDM 2023

• Search Behavior Prediction: A Hypergraph Perspective
• CL4CTR: A Contrastive Learning Framework for CTR Prediction
• IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
• Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation
• Towards Universal Cross-Domain Recommendation
• One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
• Slate-Aware Ranking for Recommendation
• Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
• Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
• Disentangled Representation for Diversified Recommendations
• Heterogeneous Graph-based Context-aware Document Ranking
• Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation
• Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking
• Self-Supervised Group Graph Collaborative Filtering for Group Recommendation
• Learning Topical Stance Embeddings from Signed Social Graphs
• Calibrated Recommendations as a Minimum-Cost Flow Problem
• Search Behavior Prediction: A Hypergraph Perspective
• DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
• Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation
• MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation
• A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping
• Disentangled Negative Sampling for Collaborative Filtering
• DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms
• SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation
• Multimodal Pre-Training with Self-Distillation for Product Understanding in E-Commerce
• Relation Preference oriented High-order Sampling for Recommendation
• Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
• Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
• Meta Policy Learning for Cold-Start Conversational Recommendation
• Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
• Simplifying Graph-based Collaborative Filtering for Recommendation
• AutoGen: An Automated Dynamic Model Generation Framework for Recommender System
• A Causal View for Item-level Effect of Recommendation on User Preference
• Federated Unlearning for On-Device Recommendation
• Counterfactual Collaborative Reasoning
• Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
• Generating Explainable Product Comparisons for Online Shopping
• Unbiased Knowledge Distillation for Recommendation
• VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation
• Knowledge-Adaptive Contrastive Learning for Recommendation
• Heterogeneous Graph Contrastive Learning for Recommendation

ICLR 2023

• Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
• ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
• Personalized Reward Learning with Interaction-Grounded Learning (IGL)
• TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
• Online Low Rank Matrix Completion
• StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
• MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

AAAI 2023

• Scaling Law for Recommendation Models: Towards General-purpose User Representations

WWW 2023

• Multi-Modal Self-Supervised Learning for Recommendation
• Collaboration-Aware Graph Convolutional Network for Recommender Systems
• Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation
• ConsRec: Learning Consensus Behind Interactions for Group Recommendation
• Semi-decentralized Federated Ego Graph Learning for Recommendation
• Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation
• Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation
• Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
• ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation
• Enhancing User Personalization in Conversational Recommenders
• LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation
• Multi-Modal Self-Supervised Learning for Recommendation
• Distillation from Heterogeneous Models for Top-K Recommendation
• On the Theories Behind Hard Negative Sampling for Recommendation
• Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
• Exploration and Regularization of the Latent Action Space in Recommendation
• Bootstrap Latent Representations for Multi-modal Recommendation
• Two-Stage Constrained Actor-Critic for Short Video Recommendation
• Recommendation with Causality enhanced Natural Language Explanations
• Cross-domain recommendation via user interest alignment
• Robust Recommendation with Adversarial Gaussian Data Augmentation
• Dual-interest Factorization-heads Attention for Sequential Recommendation
• Contrastive Collaborative Filtering for Cold-Start Item Recommendation
• Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model
• Compressed Interaction Graph based Framework for Multi-behavior Recommendation
• A Counterfactual Collaborative Session-based Recommender System
• Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
• Automated Self-Supervised Learning for Recommendation
• AutoDenoise: Automatic Data Instance Denoising for Recommendations
• Improving Recommendation Fairness via Data Augmentation
• ColdNAS: Search to Modulate for User Cold-Start Recommendation: ColdNAS
• AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
• Quantize Sequential Recommenders Without Private Data
• Interaction-level Membership Inference Attack Against Federated Recommender Systems
• Debiased Contrastive Learning for Sequential Recommendation
• Clustered Embedding Learning for Recommender Systems
• Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation
• Robust Preference-Guided Denoising for Graph based Social Recommendation
• MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations
• Few-shot News Recommendation via Cross-lingual Transfer
• User Retention-oriented Recommendation with Decision Transformer
• Cooperative Retriever and Ranker in Deep Recommenders
• Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
• Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System
• Multi-Behavior Recommendation with Cascading Graph Convolutional Network
• AutoMLP: Automated MLP for Sequential Recommendations
• NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
• Membership Inference Attacks Against Sequential Recommender Systems
• Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation
• Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
• Multi-Task Recommendations with Reinforcement Learning
• A Self-Correcting Sequential Recommender
• Cross-domain Recommendation with Behavioral Importance Perception
• Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
• Code Recommendation for Open Source Software Developers
• Denoising and Prompt-Tuning for Multi-Behavior Recommendation
• Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
• Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation
• CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation
• Dynamically Expandable Graph Convolution for Streaming Recommendation
• Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
• Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems
• Semi-supervised Adversarial Learning for Complementary Item Recommendation
• RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
• Learning with Exposure Constraints in Recommendation Systems
• Maximizing Submodular Functions for Recommendation in the Presence of Biases
• Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective
• P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
• Fairly Adaptive Negative Sampling for Recommendations

Recsys 2023

• A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
• HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation
• Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets
• Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
• Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
• Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation
• SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation
• Accelerating Creator Audience Building through Centralized Exploration
• Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
• Distribution-based Learnable Filters with Side Information for Sequential Recommendation
• Reciprocal Sequential Recommendation
• STRec: Sparse Transformer for Sequential Recommendations
• Track Mix Generation on Music Streaming Services using Transformers
• gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
• Equivariant Contrastive Learning for Sequential Recommendation
• Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation
• Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning
• Gradient Matching for Categorical Data Distillation in CTR Prediction
• Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
• AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction
• Loss Harmonizing for Multi-Scenario CTR Prediction
• When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation
• Towards Robust Fairness-aware Recommendation
• Two-sided Calibration for Quality-aware Responsible Recommendation
• RecAD: Towards A Unified Library for Recommender Attack and Defense
• Adversarial Collaborative Filtering for Free
• Augmented Negative Sampling for Collaborative Filtering
• Efficient Data Representation Learning in Google-scale Systems
• The Effect of Third Party Implementations on Reproducibility
• Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
• Trending Now: Modeling Trend Recommendations
• Investigating the effects of incremental training on neural ranking models
• Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
• LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation
• Multi-Relational Contrastive Learning for Recommendation
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
• Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback
• Alleviating the Long-Tail Problem in Conversational Recommender Systems
• Data-free Knowledge Distillation for Reusing Recommendation Models
• Reward innovation for long-term member satisfaction
• Contextual Multi-Armed Bandit for Email Layout Recommendation
• Online Matching: A Real-time Bandit System for Large-scale Recommendations
• Incentivizing Exploration in Linear Contextual Bandits under Information Gap
• AdaptEx: A Self-Service Contextual Bandit Platform
• InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach
• Correcting for Interference in Experiments: A Case Study at Douyin
• Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives
• DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender
• A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
• Exploring False Hard Negative Sample in Cross-Domain Recommendation
• Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
• Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
• Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges
• Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures
• Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation
• KGTORe: Tailored Recommendations through Knowledge-aware GNN Models
• Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation
• Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
• STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
• Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction
• BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation
• MCM: A Multi-task Pre-trained Customer Model for Personalization
• How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News
• Everyone’s a Winner! On Hyperparameter Tuning of Recommendation Models
• What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems’ Performance using Item Response Theory
• Identifying Controversial Pairs in Item-to-Item Recommendations
• A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
• ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
• Using Learnable Physics for Real-Time Exercise Form Recommendations
• ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
• Interpretable User Retention Modeling in Recommendation
• Analysis Operations for Constraint-based Recommender Systems
• Bootstrapped Personalized Popularity for Cold Start Recommender Systems
• Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model
• Personalized Category Frequency prediction for Buy It Again recommendations
• Generative Next-Basket Recommendation
• Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application
• Collaborative filtering algorithms are prone to mainstream-taste bias
• Hessian-aware Quantized Node Embeddings for Recommendation
• Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
• M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework
• Large Language Model Augmented Narrative Driven Recommendations
• Incorporating Time in Sequential Recommendation Models
• Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation
• Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
• Private Matrix Factorization with Public Item Features
• Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study
• Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation
• Extended Conversion: Capturing Successful Interactions in Voice Shopping
• On the Consistency of Average Embeddings for Item Recommendation
• Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
• Widespread Flaws in Offline Evaluation of Recommender Systems
• Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint
• Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations
• Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
• Scalable Deep Q-Learning for Session-Based Slate Recommendation
• CR-SoRec: BERT driven Consistency Regularization for Social Recommendation
• Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
• Interface Design to Mitigate Inflation in Recommender Systems
• Towards Self-Explaining Sequence-Aware Recommendation
• Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations
• Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems
• Of Spiky SVDs and Music Recommendation
• Topic-Level Bayesian Surprise and Serendipity for Recommender Systems
• Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation
• Stability of Explainable Recommendation
• Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
• Deep Exploration for Recommendation Systems
• Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
• Initiative transfer in conversational recommender systems
• Time-Aware Item Weighting for the Next Basket Recommendations
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
• Multiple Connectivity Views for Session-based Recommendation
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
• An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce
• RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems
• Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
• Visual Representation for Capturing Creator Theme in Brand-Creator Marketplace
• Station and Track Attribute-Aware Music Personalization
• Optimizing Podcast Discovery: Unveiling Amazon Music’s Retrieval and Ranking Framework
• Towards Companion Recommenders Assisting Users’ Long-Term Journeys
• Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms
• Transparently Serving the Public: Enhancing Public Service Media Values through Exploration
• Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
• Nonlinear Bandits Exploration for Recommendations
• Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry Practice
• Leveling Up the Peloton Homescreen: A System and Algorithm for Dynamic Row Ranking
• Creating the next generation of news experience on ekstrabladet.dk with recommender systems
• From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware
• Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models
• OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
• Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study
• How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective
• Leveraging Large Language Models for Sequential Recommendation
• Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation
• Learning the True Objectives of Multiple Tasks in Sequential Behavior Modeling
• Integrating Item Relevance in Training Loss for Sequential Recommender Systems
• Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?
• Uncovering ChatGPT’s Capabilities in Recommender Systems
• Continual Collaborative Filtering Through Gradient Alignment
• Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy
• Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study
• On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System Evaluation
• Climbing crags repetitive choices and recommendations
• Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServices
• Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks
• An Exploration of Sentence-Pair Classification for Algorithmic Recruiting
• Power Loss Function in Neural Networks for Predicting Click-Through Rate
• Towards Health-Aware Fairness in Food Recipe Recommendation
• A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings
• EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems
• Localify.org: Locally-focus Music Artist and Event Recommendation
• LLM Based Generation of Item-Description for Recommendation System
• Re2Dan: Retrieval of Medical Documents for e-Health in Danish
• Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit

SIGIR 2023

• A Generic Learning Framework for Sequential Recommendation with Distribution Shifts
• A Preference Learning Decoupling Framework for User Cold-Start Recommendation
• Adaptive Graph Representation Learning for Next POI Recommendation
• Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
• Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning
• Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
• Blurring-Sharpening Process Models for Collaborative Filtering
• Candidate–aware Graph Contrastive Learning for Recommendation
• Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
• Continuous Input Embedding Size Search For Recommender Systems
• Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
• Curse of “Low” Dimensionality in Recommender Systems
• Diffusion Recommender Model
• Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation
• Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
• Dynamic Graph Evolution Learning for Recommendation
• Editable User Profiles for Controllable Text Recommendations
• EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation
• Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
• Exploring scenarios of uncertainty about the users’ preferences in interactive recommendation systems
• Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity
• Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
• Generative-Contrastive Graph Learning for Recommendation
• Graph Masked Autoencoder for Sequential Recommendation
• HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation
• Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
• AutoTransfer: Instance Transfer for Cross-Domain Recommendations
• Intent-aware Ranking Ensemble for Personalized Recommendation
• It’s Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation
• Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation
• Knowledge-refined Denoising Network for Robust Recommendation
• Learning Fine-grained User Interests for Micro-video Recommendation
• LightGT: A Light Graph Transformer for Multimedia Recommendation
• LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
• LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup
• M2EU: Meta Learning for Cold-start Recommendation via Enhancing User Preference Estimation
• M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
• Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
• Graph Transformer for Recommendation
• Measuring Item Global Residual Value for Fair Recommendation
• MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
• Meta-optimized Contrastive Learning for Sequential Recommendation
• Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation
• Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation
• Aligning Distillation For Cold-start Item Recommendation
• Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
• Multi-behavior Self-supervised Learning for Recommendation
• Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation
• Multi-View Multi-Aspect Neural Networks for Next-Basket Recommendation
• Multimodal Counterfactual Learning Network for Multimedia-based Recommendation
• News Popularity Beyond the Click-Through-Rate for Personalized Recommendations
• Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network
• PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations
• Poisoning Self-supervised Learning Based Sequential Recommendations
• Prompt Learning for News Recommendation
• RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation
• Rectifying Unfairness in Recommendation Feedback Loop
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
• Single-shot Feature Selection for Multi-task Recommendations
• Spatio-Temporal Hypergraph Learning for Next POI Recommendation
• Strategy-aware Bundle Recommender System
• Time-interval Aware Share Recommendation via Bi-directional Continuous Time Dynamic Graphs
• Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation
• Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
• When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
• When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
• Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations

KDD 2023

• Generative Flow Network for Listwise Recommendation
• A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
• Text Is All You Need: Learning Language Representations for Sequential Recommendation
• MAP: A Model-agnostic Pretraining Framework for Click-Through Rate Prediction
• Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
• PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-Term User Engagement
• Efficient Bi-Level Optimization for Recommendation Denoising
• Adaptive Disentangled Transformer for Sequential Recommendation
• Path-Specific Counterfactual Fairness for Recommender Systems
• Meta Graph Learning for Long-Tail Recommendation
• Graph Neural Bandits
• E-commerce Search via Content Collaborative Graph Neural Network
• Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
• Shilling Black-Box Review-Based Recommender Systems through Fake Review Generation
• Improving Conversational Recommendation Systems via Counterfactual Data Simulation
• LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
• User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback
• Hierarchical Invariant Learning for Domain Generalization Recommendation
• UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
• Debiasing Recommendation by Learning Identifiable Latent Confounders
• Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
• Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation
• Meta Multi-Agent Exercise Recommendation: A Game Application Perspective

CIKM 2023

• Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation
• An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
• Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommendation
• Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
• 3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems
• SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems
• Popularity-aware Distributionally Robust Optimization for Recommendation System
• BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
• An Incremental Update Framework for Online Recommenders with Data-Driven Prior
• Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat
• RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
• Multi-gate Mixture-of-Contrastive-Experts with Graph-based Gating Mechanism for TV Recommendation
• Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
• A Data-Driven Index Recommendation System for Slow Queries
• Towards Improving Accuracy and Computation Cost Optimization of Recommendation Systems
• Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity
• Vigil: Effective end-to-end monitoring for large-scale recommender systems at Glance
• Timestamps as Prompts for Geography-Aware Location Recommendation
• Improving Long-Tail Item Recommendation with Graph Augmentation
• Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
• Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
• Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
• KG4Ex: An Explainable Knowledge Graph-Based Approach for Exercise Recommendation
• CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
• Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
• Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering
• HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation
• Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation
• Sequential recommendation via an adaptive cross-domain knowledge decomposition
• DPGN: Denoising Periodic Graph Network for Life Service Recommendation
• Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation
• Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
• Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation
• Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation
• Targeted Shilling Attacks on GNN-based Recommender Systems
• Decentralized Graph Neural Network for Privacy-Preserving Recommendation
• Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
• Diffusion Augmentation for Sequential Recommendation
• Prompt Distillation for Efficient LLM-based Recommendation
• Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
• Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
• iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models
• Causality-guided Graph Learning for Session-based Recommendation
• Dual-Process Graph Neural Network for Diversified Recommendation
• Modeling Preference as Weighted Distribution over Functions for User Cold-start Recommendation
• Single-User Injection for Invisible Shilling Attack against Recommender Systems
• Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
• Batch-Mix Negative Sampling for Learning Recommendation Retrievers
• Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
• SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation
• Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
• How Expressive are Graph Neural Networks in Recommendation?
• Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
• Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation
• Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
• Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations
• Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation
• Periodicity May Be Emanative: Hierarchical Contrastive Learning for Sequential Recommendation
• TPUF: Enhancing Cross-domain Sequential Recommendation via Transferring Pre-trained User Features
• Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation
• Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation
• Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation
• Dual-Oriented Contrast for Recommendation with A Stop-Gradient Operation
• CDR: Conservative Doubly Robust Learning for Debiased Recommendation
• Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective
• Dual-view Contrastive Learning for Auction Recommendation
• Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
• AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
• Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation System
• Attention Calibration for Transformer-based Sequential Recommendation
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
• CLSPRec: Contrastive Learning of Long and Short-term Preferences for Next POI Recommendation
• HAMUR: Hyper Adapter for Multi-Domain Recommendation
• Scalable Neural Contextual Bandit for Recommender Systems
• Large Language Models as Zero-Shot Conversational Recommenders
• Task-Difficulty-Aware Meta-Learning with Adaptive Update Strategies for User Cold-Start Recommendation
• AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
• Diversity-aware Deep Ranking Network for Recommendation
• Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation
• A two-tier shared embedding method for review-based recommender systems
• MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
• KuaiSAR: A Unified Search And Recommendation Dataset
• Multi-Granularity Attention Model for Group Recommendation
• Counterfactual Adversarial Learning for Recommendation
• Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
• RecRec: Algorithmic Recourse for Recommender Systems
• Graph-based Alignment and Uniformity for Recommendation
• STGIN: Spatial-Temporal Graph Interaction Network for large-scale POI recommendation
• A Flash Attention Transformer for Multi-Behaviour Recommendation
• G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender System
• DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
• MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation
• Efficient Multi-Task Learning via Generalist Recommender
• BI-GCN: Bilateral Interactive Graph Convolutional Network for Recommendation
• MvFS: Multi-view Feature Selection for Recommender System
• SeqGen: A Sequence Generator via User Side Information for Behavior Sparsity in Recommendation
• Attribute-enhanced Dual Channel Representation Learning for Session-based Recommendation
• Incorporating Co-purchase Correlation for Next-basket Recommendation
• MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation
• TCCM: Time and Content-aware Causal Model for Unbiased News Recommendation
• Personalized Interest Sustainability Modeling for Sequential POI Recommendation
• Boosting Meta-learning Cold-start Recommendation with Graph Neural Network
• Modeling Sequential Collaborative User Behaviors For Seller-Aware Next Basket Recommendation
• PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering
• Retrieving GNN Architecture for Collaborative Filtering
• Toward a Better Understanding of Loss Functions for Collaborative Filtering
• Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
• Geometry Interaction Augmented Graph Collaborative Filtering

IJCAI 2023

• Self-supervised Graph Disentangled Networks for Review-based Recommendation
• Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
• Sequential Recommendation with Probabilistic Logical Reasoning
• Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation
• Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation
• Probabilistic Masked Attention Networks for Explainable Sequential Recommendation
• Dual Personalization on Federated Recommendation
• Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation
• Discriminative-Invariant Representation Learning for Unbiased Recommendation
• Denoised Self-Augmented Learning for Social Recommendation
• Intent-aware Recommendation via Disentangled Graph Contrastive Learning

ICML 2023

• Vertical Federated Graph Neural Network for Recommender System
• Propensity Matters: Measuring and Enhancing Balancing for Recommendation
• Performative Recommendation: Diversifying Content via Strategic Incentives
• Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation

NeurIPS 2023

• Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
• Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints
• Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
• Recommender Systems with Generative Retrieval
• Lending Interaction Wings to Recommender Systems with Conversational Agents
• Supply-Side Equilibria in Recommender Systems
• Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
• Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
• Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation
• Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
• UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
• Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
• Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
• REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
• On the Relationship Between Relevance and Conflict in Online Social Link Recommendations
• Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
• Cascading Bandits: Optimizing Recommendation Frequency in Delayed Feedback Environments

2022

SIGIR 2022

• Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems
• Decoupled Side Information Fusion for Sequential Recommendation
• Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
• Interpolative Distillation for Unifying Biased and Debiased Recommendation
• Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation
• Unify Local and Global Information for Top-N Recommendation
• Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders
• User-Aware Multi-Interest Learning for Candidate Matching in Recommenders
• Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
• Multi-Level Interaction Reranking with User Behavior History
• User-controllable Recommendation Against Filter Bubbles
• DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation
• Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
• A Review-aware Graph Contrastive Learning Framework for Recommendation
• Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
• On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
• Multi-Agent RL-based Information Selection Model for Sequential Recommendation
• Knowledge Graph Contrastive Learning for Recommendation
• Enhancing CTR Prediction with Context-Aware Feature Representation Learning
• Joint Multisided Exposure Fairness for Recommendation
• When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
• Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
• Single-shot Embedding Dimension Search in Recommender System
• Learning to Infer User Implicit Preference in Conversational Recommendation
• Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation
• An Attribute-Driven Mirroring Graph Network for Session-based Recommendation
• Geometric Disentangled Collaborative Filtering
• Hypergraph Contrastive Collaborative Filtering
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• User-Centric Conversational Recommendation with Multi-Aspect User Modeling
• MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations
• HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
• Webformer: Pre-training with Web Pages for Information Retrieval
• Forest-based Deep Recommender
• Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
• Privacy-Preserving Synthetic Data Generation for Recommendation
• Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
• DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph
• Variational Reasoning about User Preferences for Conversational Recommendation
• Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator
• NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction
• Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
• Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems
• HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
• Self-Guided Learning to Denoise for Robust Recommendation
• AutoGSR: Neural Architecture Search for Graph-based Session Recommendation
• Learning Graph-based Disentangled Representations for Next POI Recommendation
• GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
• Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
• Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering
• INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
• ProFairRec: Provider Fairness-aware News Recommendation
• Multi-Faceted Global Item Relation Learning for Session-Based Recommendation
• ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping
• Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer
• CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users
• Multi-Behavior Sequential Transformer Recommender
• Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates
• Explainable Fairness for Feature-aware Recommender Systems
• Graph Trend Filtering Networks for Recommendation
• AutoLossGen: Automatic Loss Function Generation for Recommender Systems
• Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation
• KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums
• CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
• PEVAE: A hierarchical VAE for personalized explainable recommendation.
• Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
• Less is More: Reweighting Important Spectral Graph Features for Recommendation

RecSys 2022

• A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models
• A User-Centered Investigation of Personal Music Tours
• Adversary or Friend? An adversarial Approach to Improving Recommender Systems
• Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation
• BRUCE – Bundle Recommendation Using Contextualized item Embeddings
• Bundle MCR: Towards Conversational Bundle Recommendation
• CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
• Context and Attribute-Aware Sequential Recommendation via Cross-Attention
• Countering Popularity Bias by Regularizing Score Differences
• Defending Substitution-based Profile Pollution Attacks on Sequential Recommenders
• Denoising Self-Attentive Sequential Recommendation
• Don’t recommend the obvious: estimate probability ratios
• Dual Attentional Higher Order Factorization Machines
• Dynamic Global Sensitivity for Differentially Private Contextual Bandits
• EANA: Reducing Privacy Risk on Large-scale Recommendation Models
• Effective and Efficient Training for Sequential Recommendation using Recency Sampling
• Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
• Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
• Fairness-aware Federated Matrix Factorization
• Fast And Accurate User Cold-Start Learning Using Monte Carlo Tree Search
• Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
• Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy
• Learning Recommendations from User Actions in the Item-poor Insurance Domain
• Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
• MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer
• Modeling Two-Way Selection Preference for Person-Job Fit
• Modeling User Repeat Consumption Behavior for Online Novel Recommendation
• Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation
• Off-Policy Actor Critic for Recommender Systems
• ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
• RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
• Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation
• Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales
• Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
• Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
• Towards Psychologically Grounded Dynamic Preference Models
• You Say Factorization Machine, I Say Neural Network – It’s All in the Activation
• Revisiting the Performance of iALS on Item Recommendation Benchmarks

KDD 2022

• Comprehensive Fair Meta-learned Recommender System
• Graph-Flashback Network for Next Location Recommendation
• Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
• GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
• Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
• Practical Counterfactual Policy Learning for Top-K Recommendations
• Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
• Towards Representation Alignment and Uniformity in Collaborative Filtering
• Knowledge-enhanced Black-box Attacks for Recommendations
• Towards Universal Sequence Representation Learning for Recommender Systems
• Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems
• Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
• User-Event Graph Embedding Learning for Context-Aware Recommendation
• Aligning Dual Disentangled User Representations from Ratings and Textual Content
• Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
• Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution
• Invariant Preference Learning for General Debiasing in Recommendation
• PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions
• CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
• HICF: Hyperbolic Informative Collaborative Filtering
• Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation
• Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification
• Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
• Self-Supervised Hypergraph Transformer for Recommender Systems
• PinnerFormer: Sequence Modeling for User Representation at Pinterest
• TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation

CIKM 2022

• A Biased Sampling Method for Imbalanced Personalized Ranking
• Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
• Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
• An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering
• Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation
• Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
• Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainabilit
• ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
• Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
• Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
• Cross-domain Recommendation via Adversarial Adaptation
• Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
• Domain-Agnostic Constrastive Representations for Learning from Label Proportions
• Dual-Task Learning for Multi-Behavior Sequential Recommendation
• Dynamic Causal Collaborative Filtering
• Dynamic Hypergraph Learning for Collaborative Filtering
• Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
• Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation
• Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation
• Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
• HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
• ITSM-GCN: Informative Training Sample Mining for Graph Convolution Network-based Collaborative Filtering
• KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
• MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation
• Memory Bank Augmented Long-tail Sequential Recommendation
• Multi-level Contrastive Learning Framework for Sequential Recommendation
• OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
• Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
• Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
• SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
• Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks
• MAE4Rec: Storage-saving Transformer for Sequential Recommendations
• Target Interest Distillation for Multi-Interest Recommendation
• The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation
• Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation
• Towards Principled User-side Recommender Systems
• Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models
• Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference

WSDM 2022

• Long Short-Term Temporal Meta-learning in Online Recommendation
• Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
• RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
• Personalized Transfer of User Preferences for Cross-domain Recommendation
• Graph Collaborative Reasoning
• Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
• Enumerating Fair Packages for Group Recommendations
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• CAN: Feature Co-Action Network for Click-Through Rate Prediction
• VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering
• On Sampling Collaborative Filtering Datasets
• Triangle Graph Interest Network for Click-through Rate Prediction
• Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
• S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks
• Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
• Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
• Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
• Supervised Advantage Actor-Critic for Recommender Systems
• Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
• Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining
• C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
• Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation
• A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation
• Profiling the Design Space for Graph Neural Networks based Collaborative Filtering
• Towards Unbiased and Robust Causal Ranking for Recommender Systems
• Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation
• Contrastive Meta Learning with Behavior Multiplicity for Recommendation

WWW 2022

• Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
• A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
• CAUSPref: Causal Preference Learning for Out-of-Distribution Recommendation
• FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
• Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
• Modeling User Behavior with Graph Convolution for Personalized Product Search
• Optimizing Rankings for Recommendation in Matching Markets
• PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation
• Path Language Modeling over Knowledge Graphs for Explainable Recommendation
• Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
• Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
• Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making
• AutoField: Automating Feature Selection in Deep Recommender Systems
• CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction
• Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations
• Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
• Cross Pairwise Ranking for Unbiased Item Recommendation
• Deep Unified Representation for Heterogeneous Recommendation
• Disentangling Long and Short-Term Interests for Recommendation
• Efficient Online Learning to Rank for Sequential Music Recommendation
• Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
• FeedRec: News Feed Recommendation with Various User Feedbacks
• Filter-enhanced MLP is All You Need for Sequential Recommendation
• FIRE: Fast Incremental Recommendation with Graph Signal Processing
• Generative Session-based Recommendation
• Graph Neural Transport Networks with Non-local Attentions for Recommender Systems
• Graph-based Extractive Explainer for Recommendations
• GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction
• Hypercomplex Graph Collaborative Filtering
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
• Intent Contrastive Learning for Sequential Recommendation
• Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
• Learning Robust Recommenders through Cross-Model Agreement
• Learning to Augment for Casual User Recommendation
• MCL: Mixed-Centric Loss for Collaborative Filtering
• MINDSim: User Simulator for News Recommenders
• Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation
• Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
• Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
• Off-policy Learning over Heterogeneous Information for Recommendation
• Rating Distribution Calibration for Selection Bias Mitigation in Recommendations
• Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
• Sequential Recommendation via Stochastic Self-Attention
• Sequential Recommendation with Decomposed Item Feature Routing
• Stochastic-Expert Variational Autoencoder for Collaborative Filtering
• Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
• Unbiased Sequential Recommendation with Latent Confounders
• A Contrastive Sharing Model for Multi-Task Recommendation
• Accurate and Explainable Recommendation via Review Rationalization
• Comparative Explanations of Recommendations
• Recommendation Unlearning
• STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation
• VisGNN: Personalized Visualization Recommendation via Graph Neural Networks
• Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation
• Causal Representation Learning for Out-of-Distribution Recommendation
• End-to-end Learning for Fair Ranking Systems
• Following Good Examples – Health Goal-Oriented Food Recommendation based on Behavior Data
• Link Recommendations for PageRank Fairness
• DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction
• DC-GNN: Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval
• Simgrace: A Simple Framework for graph contrastive learning without data augmentation

IJCAI 2022

• Towards Resolving Propensity Contradiction in Offline Recommender Learning
• MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
• Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback
• HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
• Enhancing Sequential Recommendation with Graph Contrastive Learning
• RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
• Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
• Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation
• Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
• Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
• Self-supervised Graph Neural Networks for Multi-behavior Recommendation
• Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

ICML 2022

• Estimating and Penalizing Induced Preference Shifts in Recommender Systems
• Learning from a Learning User for Optimal Recommendations

Recommendation

Models

2023

• DCAH: Search Behavior Prediction: A Hypergraph Perspective (WSDM'23)
• CLUE: Scaling Law for Recommendation Models: Towards General-purpose User Representations (AAAI'23)

2022

• PinnerFormer: PinnerFormer: Sequence Modeling for User Representation at Pinterest (KDD'22)
• ItemSage: ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (KDD'22)
• DuoRec: Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (WSDM'22)
• FMLP-Rec: FilterEnhanced MLP is All You Need for Sequential Recommendation (WWW'22)
• CML: Contrastive Meta Learning with Behavior Multiplicity for Recommendation (WSDM'22)
• Tiger: Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation (CIKM'22)
• AiRS: AiRS: A Large-Scale Recommender System at NAVER News (ICDE'22)
• CoRGi: CORGI: Content-Rich Graph Neural Networks with Attention (KDD'22)
• SimGCL: Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation (SIGIR'22)
• HCCF: Hypergraph Contrastive Collaborative Filtering (SIGIR'22)
• NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (WWW'22)
• SHT: Self-Supervised Hypergraph Transformer for Recommender Systems (KDD'22)
• SimGRACE: Simgrace: A Simple Framework for graph contrastive learning without data augmentation (WWW'22)
• AutoGCL: Autogcl: Automated graph contrastive learning via learnable view generators (AAAI'22)
• SAIL: SAIL: Self-Augmented Graph Contrastive Learning (AAAI'22)
• UniSRec: Towards Universal Sequence Representation Learning for Recommender Systems (KDD'22)

2021

• MAIL: Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems (CIKM'21)
• Transformer4Rec: Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation (Recsys'21)
• NGF: Neural graph filtering for context-aware recommendation (ACML'21)
• SGL: Self-supervised Graph Learning for Recommendation (SIGIR'21)
• MHCN: Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (WWW'21)
• DHCN: Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (AAAI'21)
• MINCE: Memory Augmented MultiInstance Contrastive Predictive Coding for Sequential Recommendation (ICDM'21)
• SEPT: Socially-Aware Self-Supervised Tri-Training for Recommendation (KDD'21)
• BUIR: Bootstrapping User and Item Representations for One-Class Collaborative Filtering (SIGIR'21)
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (CIKM'21)
• MixGCF: MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems (KDD'21)
• SERec: An Efficient and Effective Framework for Session-based Social Recommendation (WSDM'21)
• CL4SRec: Contrastive Learning for Sequential Recommendation (SIGIR'21)
• GCA: Graph Contrastive Learning with Adaptive Augmentation (WWW'21)

2020

• PinnerSage: PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest (KDD'20)
• TAFA: TAFA: Two-headed attention fused autoencoder for context-aware recommendations (Recsys'20)
• MBCN: Multi-Branch Convolutional Network for Context-Aware Recommendation (SIGIR'20)
• ENSFM: Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation (WWW'20)
• S3-Rec: S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (CIKM'20)
• DMR: Deep Match to Rank Model for Personalized Click-Through Rate Prediction (AAAI'20)
• EHCF: Efficient heterogeneous collaborative filtering without neg-ative sampling for recommendation (AAAI'20)
• SCE-GNN: Global Context Enhanced Graph Neural Networks for Session-based Recommendation (SIGIR'20)
• SSG: Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation (CIKM'20)
• SML: Symmetric Metric Learning with Adaptive Margin for Recommendation (AAAI'20)
• KHGT: Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation (AAAI'20)
• LCF: Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (PMLR'20)
• SEE-PT: SEE-PT: Sequential recommendation via personalized transformer (RecSys'20)
• LightGCN: LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (SIGIR'20)
• MBGCN: Multi-behavior Recommendation with Graph Convolutional Networks (SIGIR'20)
• MA-GNN: Memory Augmented Graph Neural Networks for Sequential Recommendation (AAAI'20)
• GCCF: Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (AAAI'20)
• HyRec: Next-item Recommendation with Sequential Hypergraphs (SIGIR'20)
• DGCF: Disentangled Graph Collaborative Filtering (SIGIR'20)
• GRACE: Deep Graph Contrastive Representation Learning (arXiv'20)

2019

• LLAE: From Zero-Shot Learning to Cold-Start Recommendation (AAAI'19)
• MetaEmb: Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings (SIGIR'19)
• NMTR: Neural Multi-Task Recommendation from Multi-Behavior Data (ICDE'19)
• NGCF: Neural Graph Collaborative Filtering (SIGIR'19)
• BERT4Rec: BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (CIKM'19)
• KGAT: KGAT: Knowledge Graph Attention Network for Recommendation (KDD'19)
• KGCN: Knowledge Graph Convolutional Networks for Recommender Systems (WWW'19)
• GraphRec: Graph Neural Networks for Social Recommendation (WWW'19)
• NARRE: Neural Attentional Rating Regression with Review-level Explanations (WWW'19)
• METAS: Action Space Learning for Heterogeneous User Behavior Prediction (IJCAI'19)
• SR-GNN: Session-based recommendation with graph neural networks (AAAI'19)
• SelCa: Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling (CIKM'19)
• MMGCN: MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video (MM'19)

2018

• Caser: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (WSDM'18)
• PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (KDD'18)
• HIN: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (CIKM'18)
• VAE: Variational Autoencoders for Collaborative Filtering (WWW'18)
• triple2vec: Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty (CIKM'18)
• GC-MC: Graph Convolutional Matrix Completion (KDD'18)
• SASRec: Self-Attentive Sequential Recommendation (ICDM'18)
• SDNets: Adversarial Distillation for Efficient Recommendation with External Knowledge (TOIS'18)
• AIN: An Attentive Interaction Network for Context-aware Recommendation (CIKM'18)
• ConvNCF: Outer Product-based Neural Collaborative Filtering (IJCAI'18)
• STAMP: STAMP: shortterm attention/memory priority model for session-based recommendation (KDD'18)
• A3CF: An Adaptive Aspect Attention Model for Rating Prediction (IJCAI'18)

2017

• TransNet: TransNets: Learning to Transform for Recommendation (Recsys'17)
• DeepCoNN: Joint Deep Modeling of Users and Items Using Reviews for Recommendation (WSDM'17)
• ACF: Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (SIGIR'17)
• CML: Collaborative Metric Learning (WWW'17)
• NMF: Neural Factorization Machines for Sparse Predictive Analytics (SIGIR'17)
• DMF: Deep matrix factorization models for recommender systems (IJCAI'17)
• NARM: Neural attentive session-based recommendation (CIKM'17)
• NCF: Neural Collaborative Filtering (WWW'17)
• GRU: Sequential User-based Recurrent Neural Network Recommendations (RecSys'17)

~2016

• CDAE: Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (WSDM'16)
• DREAM: A Dynamic Recurrent Model for Next Basket Recommendation (SIGIR'16)
• ConvMF: Convolutional Matrix Factorization for Document Context-Aware Recommendation (RecSys'16)
• eALS: Fast Matrix Factorization for Online Recommendation with Implicit Feedback (SIGIR'16)
• GRU4Rec: Session-based Recommendations with Recurrent Neural Networks (ICLR'16)
• AutoRec: AutoRec: Autoencoders Meet Collaborative Filtering (WWW'15)
• CDL: Collaborative Deep Learning for Recommender Systems (KDD'15)
• CSLIM: Deviation-Based Contextual SLIM Recommenders (CIKM'14)
• LogisticMF: Logistic Matrix Factorization for Implicit Feedback (NeurIPS'14)
• HFT: Hidden factors and hidden topics: understanding rating dimensions with review text (Recsys'13)
• CTR: Collaborative topic modeling for recommending scientific articles (KDD'11)
• SLIM: SLIM: Sparse Linear Methods for Top-N Recommender Systems (ICDM'11)
• MF: Matrix factorization techniques for recommender systems (MC'09)
• BPR: BPR: Bayesian Personalized Ranking from Implicit Feedback (UAI'09)
• SoRec: SoRec: Social Recommendation Using Probabilistic Matrix Factorization (CIKM'08)
• ALS: Collaborative Filtering for Implicit Feedback Datasets (ICDM'08)
• RBM: Restricted Boltzmann Machines for Collaborative Filtering (ICML'07)
• Item-Base CF: Item-based top-N recommendation algorithms (TOIS'04)

Others

• Self-supervised Learning for Large-scale Item Recommendations (CIKM'21)
• Disentangled Self-Supervision in Sequential Recommenders (KDD'20)
• The YouTube video recommendation system (Recsys'16)
• Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering (Recsys'10)
• The Netflix prize (KDD'07)
• Amazon.com recommendations: item-to-item collaborative filtering (MIC'03)
• Item-based collaborative filtering recommendation algorithms (WWW'01)
• Learning Collaborative Information Filters (AAAI'98)
• GroupLens: An Open Architecture for Collaborative Filtering of Netnews (CSCW'94)

Survey

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2022)
• Deep Learning Based Recommender System: A Survey and New Perspectives (2019)
• Recommender System Application Developments: A Survey (2015)