mlsanityData to 7 Oct 2026

Trustworthy Recommender Systems

arXiv 2208.06265 ↗First posted 10 Aug 2022cs.IR cs.CR cs.LGcited by 21

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Abstract (arXiv)

Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 26 of its 92 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

26 of 92 references.

References · 26

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  3. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  4. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 246
  5. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  6. 2019Large Scale Adversarial Representation Learningcited by 238
  7. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  8. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  9. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
  10. 2019Learning Disentangled Representations for Recommendationcited by 110
  11. 2022A Survey on the Fairness of Recommender Systemscited by 102
  12. 2019A Survey on Session-based Recommender Systemscited by 97
14 more
  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  3. 2021AutoDebias: Learning to Debias for Recommendationcited by 68
  4. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  5. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  6. 2020LoCo: Local Contrastive Representation Learningcited by 55
  7. 2014word2vec Parameter Learning Explainedcited by 53
  8. 2022A Survey on Trustworthy Recommender Systemscited by 40
  9. 2018Facial Expression Analysis under Partial Occlusion: A Surveycited by 28
  10. 2018User Fairness in Recommender Systemscited by 20
  11. 2022Explainable Fairness in Recommendationcited by 15
  12. 2022ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferencescited by 15
  13. 2022The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendationcited by 9
  14. 2013ReputationPro: The Efficient Approaches to Contextual Transaction Trust Computation in E-Commerce Environmentscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 8; 2026: 4.

21 papers.

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  1. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  2. 2024FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learningcited by 8
  3. 2024Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Trainingcited by 3
  4. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  5. 2026In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generationscited by 3
  6. 2024The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judgescited by 2
  7. 2024Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matterscited by 2
  8. 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
  9. 2024Heterogeneous Hypergraph Embedding for Recommendation Systemscited by 1
  10. 2025Recommending the right academic programs: An interest mining approach using BERTopiccited by 1
  11. 2025We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Latercited by 1
  12. 2023Trustworthy Large Models in Vision: A Surveycited by 0
9 more
  1. 2024A Simple Yet Effective Approach for Diversified Session-Based Recommendationcited by 0
  2. 2024Stability of Explainable Recommendationcited by 0
  3. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  4. 2025When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMscited by 0
  5. 2025Point of Interest Recommendation: Pitfalls and Viable Solutionscited by 0
  6. 2025DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendationcited by 0
  7. 2025Empowering Denoising Sequential Recommendation with Large Language Model Embeddingscited by 0
  8. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  9. 2026VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agentscited by 0