# Trustworthy Recommender Systems

arXiv: https://arxiv.org/abs/2208.06265 · HTML: https://mlsanity.com/paper/2208.06265/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 92 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 26 of 26)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Large Scale Adversarial Representation Learning](https://mlsanity.com/paper/1907.02544/) (2019)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [AutoDebias: Learning to Debias for Recommendation](https://mlsanity.com/paper/2105.04170/) (2021)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [LoCo: Local Contrastive Representation Learning](https://mlsanity.com/paper/2008.01342/) (2020)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [A Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2207.12515/) (2022)
- [Facial Expression Analysis under Partial Occlusion: A Survey](https://mlsanity.com/paper/1802.08784/) (2018)
- [User Fairness in Recommender Systems](https://mlsanity.com/paper/1807.06349/) (2018)
- [Explainable Fairness in Recommendation](https://mlsanity.com/paper/2204.11159/) (2022)
- [ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences](https://mlsanity.com/paper/2207.13897/) (2022)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [ReputationPro: The Efficient Approaches to Contextual Transaction Trust Computation in E-Commerce Environments](https://mlsanity.com/paper/1311.6543/) (2013)

## Built on by (most cited first, 21 of 21)

- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning](https://mlsanity.com/paper/2410.17555/) (2024)
- [In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generations](https://mlsanity.com/paper/2602.15456/) (2026)
- [Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training](https://mlsanity.com/paper/2410.20965/) (2024)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges](https://mlsanity.com/paper/2411.15396/) (2024)
- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems](https://mlsanity.com/paper/2603.28476/) (2026)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)
- [Recommending the right academic programs: An interest mining approach using BERTopic](https://mlsanity.com/paper/2501.06581/) (2025)
- [We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later](https://mlsanity.com/paper/2509.09414/) (2025)
- [Trustworthy Large Models in Vision: A Survey](https://mlsanity.com/paper/2311.09680/) (2023)
- [A Simple Yet Effective Approach for Diversified Session-Based Recommendation](https://mlsanity.com/paper/2404.00261/) (2024)
- [Stability of Explainable Recommendation](https://mlsanity.com/paper/2405.01849/) (2024)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)
- [When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs](https://mlsanity.com/paper/2507.05733/) (2025)
- [Point of Interest Recommendation: Pitfalls and Viable Solutions](https://mlsanity.com/paper/2507.13725/) (2025)
- [DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation](https://mlsanity.com/paper/2507.13957/) (2025)
- [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings](https://mlsanity.com/paper/2510.04239/) (2025)
- [Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems](https://mlsanity.com/paper/2606.25533/) (2026)
- [VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents](https://mlsanity.com/paper/2609.38270/) (2026)

## 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.