mlsanityData to 14 Oct 2026

A Comprehensive Survey on Trustworthy Recommender Systems

arXiv 2209.10117 ↗First posted 21 Sep 2022cs.IR cs.AI cs.CRcited by 32

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Abstract (arXiv)

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites. In the past few decades, the rapid developments of recommender systems have significantly benefited human by creating economic value, saving time and effort, and promoting social good. However, recent studies have found that data-driven recommender systems can pose serious threats to users and society, such as spreading fake news to manipulate public opinion in social media sites, amplifying unfairness toward under-represented groups or individuals in job matching services, or inferring privacy information from recommendation results. Therefore, systems' trustworthiness has been attracting increasing attention from various aspects for mitigating negative impacts caused by recommender systems, so as to enhance the public's trust towards recommender systems techniques. In this survey, we provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 244 of its 461 references that are in the map. First posted on arXiv 21 Sep 2022.

Builds on

244 of 461 references.

References · 244

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2017Billion-scale similarity search with GPUscited by 2,497
  7. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  8. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  9. 2020Knowledge Distillation: A Surveycited by 1,603
  10. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  11. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  12. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
38 more
  1. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  2. 2016Adversarial Machine Learning at Scalecited by 936
  3. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  4. 2018LEAF: A Benchmark for Federated Settingscited by 792
  5. 2019Graph Neural Networks for Social Recommendationcited by 714
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  7. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  8. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  9. 2019Federated Machine Learning: Concept and Applicationscited by 412
  10. 2017Algorithmic decision making and the cost of fairnesscited by 403
  11. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  12. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  13. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 376
  14. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  15. 2019ERNIE: Enhanced Representation through Knowledge Integrationcited by 302
  16. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 301
  17. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  18. 2018Knockoff Nets: Stealing Functionality of Black-Box Modelscited by 292
  19. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  20. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  21. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  22. 2021Whitening Sentence Representations for Better Semantics and Faster Retrievalcited by 202
  23. 2018Fairness of Exposure in Rankingscited by 190
  24. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  25. 2020Embedding-based Retrieval in Facebook Searchcited by 156
  26. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  27. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  28. 2020A Survey of Privacy Attacks in Machine Learningcited by 146
  29. 2018The Frontiers of Fairness in Machine Learningcited by 141
  30. 2019Privacy Risks of Securing Machine Learning Models against Adversarial Examplescited by 141
  31. 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 138
  32. 2020RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithmscited by 138
  33. 2020To be Robust or to be Fair: Towards Fairness in Adversarial Trainingcited by 137
  34. 2013Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifierscited by 130
  35. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  36. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  37. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  38. 2019Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learningcited by 122

50 most cited of 244 All 244 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 9; 2025: 8; 2026: 8.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  3. 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applicationscited by 111
  4. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  5. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 61
  6. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  7. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  8. 2025CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agentcited by 25
  9. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  10. 2024Fairness Definitions in Language Models Explainedcited by 11
  11. 2023Multi-agent Attacks for Black-box Social Recommendationscited by 9
  12. 2026In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generationscited by 6
20 more
  1. 2025Exposing Privacy Risks in Graph Retrieval-Augmented Generationcited by 5
  2. 2024ToDA: Target-oriented Diffusion Attacker against Recommendation Systemcited by 4
  3. 2025Fidelity-Aware Recommendation Explanations via Stochastic Path Integrationcited by 3
  4. 2026From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agentscited by 3
  5. 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directionscited by 2
  6. 2024Joint Universal Adversarial Perturbations with Interpretationscited by 2
  7. 2025Exposing Citation Vulnerabilities in Generative Enginescited by 2
  8. 2026RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendationcited by 2
  9. 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
  10. 2023Defense Against Model Extraction Attacks on Recommender Systemscited by 1
  11. 2024Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systemscited by 1
  12. 2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendationscited by 1
  13. 2025Traceable Drug Recommendation over Medical Knowledge Graphscited by 1
  14. 2024Stability of Explainable Recommendationcited by 0
  15. 2024Fuzzy Norm-Explicit Product Quantization for Recommender Systemscited by 0
  16. 2025Shilling Recommender Systems by Generating Side-feature-aware Fake User Profilescited by 0
  17. 2026Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoringcited by 0
  18. 2026MedicalRec: Medical recommender system for image classification without retrainingcited by 0
  19. 2026Rethinking Fairness in LLM-Based Recommender Systems: A Surveycited by 0
  20. 2026RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intentcited by 0