Awesome-Paper-List

• Self-Supervised Recommendation
• On Device Recommendation
• Graph Representation Learning and Inference
• Decentralized/Federated Recommendation
• Multimodal Recommendation
• Causal Learning and Inference

Self-Supervised Recommendation

Year 2022

• [preprint] Self-Supervised Learning for Recommender Systems: A Survey paper
• [preprint] Towards Extremely Simple Graph Contrastive Learning for Recommendation paper code
• [KDD] Contrastive Cross-domain Recommendation in Matching paper code
• [KDD] Towards Universal Sequence Representation Learning for Recommender Systems paper code
• [SIGIR] Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation paper code
• [WWW] Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning paper code
• [WWW] Intent Contrastive Learning for Sequential Recommendation paper code
• [WSDM] Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation paper code
• [ICDE] Contrastive Learning for Sequential Recommendation paper
• [preprint] Improving Contrastive Learning with Model Augmentation paper code

Year 2021

• [SIGIR] Self-supervised Graph Learning for Recommendation paper code
• [WWW] Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation paper code
• [KDD] Socially-Aware Self-Supervised Tri-training for Recommendation paper code
• [AAAI] Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation paper code
• [CIKM] Self-Supervised Graph Co-training for Session-based Recommendation paper code
• [CIKM] Self-supervised Learning for Large-scale Item Recommendations paper
• [preprint] Contrastive Self-supervised Sequential Recommendation with Robust Augmentation paper code
• [SIGIR] Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer paper code
• [SIGIR] Bootstrapping User and Item Representations for One-Class Collaborative Filtering paper code
• [WSDM] Bipartite graph embedding via mutual information maximization paper code
• [WSDM] Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation paper code
• [preprint] One4all User Representation for Recommender Systems in E-commerce paper
• [preprint] Scaling Law for Recommendation Models: Towards General-purpose User Representations paper

Year 2020

• [CIKM] S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization paper code

On-Device Recommendation

Year 2022

• [preprint] Efficient On-device Session-based Recommendation paper code
• [SIGIR] On-Device Next-item Recommendation with Self-supervised Knowledge Distillation paper code

Year 2021

• [KDD] Learning Elastic Embeddings for Customizing On-device Recommenders paper
• [WWW] DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce paper code
• [TIOT] A Survey of On-device Machine Learning: An Algorithms and Learning Theory Perspective paper

Year 2020

• [WWW] Next Point-of-Interest Recommendation on Resource-constrained Mobile Devices paper

Year 2019

• [CARS] On-device User Intent Prediction for Context and Sequence Aware Recommendation paper
• [KDD] Real-time On-device Troubleshooting Recommendation for Smartphones paper
• [preprint] On-device Neural Net Inference with Mobile GPUs paper

Graph Representation Learning and Inference

Year 2022

• [KDD] Graph Attention Multi-Layer Perceptron paper code

Year 2021

• [ICLR] Simple Spectral Graph Convolution paper code
• [NeurIPS] Node Dependent Local Smoothing for Scalable Graph Learning paper code
• [ICML] GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings paper code
• [PVLDB] Accelerating Large Scale Real-Time GNN Inference using Channel Pruning paper code

Year 2020

• [NeurIPS] Scalable Graph Neural Networks via Bidirectional Propagation paper code
• [ICML] SIGN: Scalable Inception Graph Neural Networks paper code
• [KDD] Scaling Graph Neural Networks with Approximate PageRank paper code
• [ICLR] GraphSAINT: Graph Sampling Based Inductive Learning Method paper code

Year 2019

• [ICML] Simplifying Graph Convolutional Networks paper code
• [KDD] Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks paper code

Year 2018

• [KDD] Graph Convolutional Neural Networks for Web-Scale Recommender Systems paper
• [KDD] Large-Scale Learnable Graph Convolutional Networks paper code
• [ICLR] FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling paper code
• [ICML] Stochastic Training of Graph Convolutional Networks with Variance Reduction paper code
• [NeurIPS] Adaptive Sampling Towards Fast Graph Representation Learning paper code_pytorch code_tentsor_flow

Year 2017

• [NeurIPS] Inductive Representation Learning on Large Graphs paper code

Decentralized/Federated Recommendation

Year 2022

• [Preprint] LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization paper
• [Preprint] FedRule: Federated Rule Recommendation System with Graph Neural Networks paper
• [Preprint] Practical and Secure Federated Recommendation with Personalized Masks paper
• [Preprint] FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation paper
• [Preprint] FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling paper
• [Nature Communications] A federated graph neural network framework for privacy-preserving personalization paper code
• [Knowledge-based Systems] Federated Neural Collaborative Filtering paper
• [TIST] Federated Social Recommendation with Graph Neural Network paper
• [Preprint] FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings paper
• [JIIS] User-controlled federated matrix factorization for recommender systems paper

Year 2021

• [WSDM] PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion paper
• [WWW] Hierarchical Personalized Federated Learning for User Modeling paper
• [VLDBJ] Fast-adapting and Privacy-preserving Federated Recommender System paper
• [Preprint] Learning Federated Representations and Recommendations with Limited Negatives paper
• [RecSys] Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback paper
• [SIGIR] FedCT: Federated Collaborative Transfer for Recommendation paper
• [RecSys] FR-FMSS: Federated Recommendation via Fake Marks and Secret Sharing paper
• [SIGIR] Meta Matrix Factorization for Federated Rating Predictions paper
• [IEEE IS] FedRec: Federated Recommendation With Explicit Feedback paper
• [AAAI] FedRec++: Lossless Federated Recommendation with Explicit Feedback paper
• [RecSys] A Payload Optimization Method for Federated Recommender Systems paper
• [RecSys] Horizontal Cross-Silo Federated Recommender Systems paper
• [ECML-PKDD] Federated Multi-view Matrix Factorization for Personalized Recommendations paper
• [ECIR] Robustness of Meta Matrix Factorization Against Strict Privacy Constraints paper
• [IEEE IS] Secure Federated Matrix Factorization paper code

Year 2020

• [EMNLP] Privacy-Preserving News Recommendation Model Learning paper
• [KDD] FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems paper
• [ISIT] Federated Recommendation System via Differential Privacy paper
• [SRDS] On the Detection of Shilling Attacks in Federated Collaborative Filtering paper
• [Preprint] Robust Federated Recommendation System paper

Year 2019

• [Preprint] Federated Meta-Learning with Fast Convergence and Efficient Communication paper code
• [Preprint] Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System paper

Multimodal Recommendation

Causal Learning and Inference

Year 2022

• [Preprint] To Impute or not to Impute? Missing Data in Treatment Effect Estimation paper
• [NeurIPS] Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions paper

Year 2021

• [Preprint] On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty paper
• [ICML] Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding paper
• [TKDD] A Survey on Causal Inference paper

Year 2020

• [ICML] DeepMatch: balancing deep covariate representations for causal inference using adversarial training paper
• [NeurIPS] Identifying causal-effect inference failure with uncertainty-aware models paper
• [UAI] Identifying causal-effect inference failure with uncertainty-aware models paper
• [NeurIPS] Counterfactual predictions under runtime confounding paper

Year 2019

• [NeurIPS] Adapting neural networks for the estimation of treatment effects paper

Year 2018

• [NeurIPS] Causal effect inference with deep latent-variable models paper
• [NeurIPS] Representation learning for treatment effect estimation from observational data paper
• [ICLR] GANITE: Estimation of Individual Treatment Effects Using Generative Adversarial Nets paper

Year 2017

• [ICML] Estimating individual treatment effect: generalization bounds and algorithms paper
• [NeurIPS] Bayesian inference of individualized treatment effects using multi-task Gaussian processes paper

Year 2016

• [ICML] Learning Representations for Counterfactual Inference paper