# A Survey on Trustworthy Recommender Systems

arXiv: https://arxiv.org/abs/2207.12515 · HTML: https://mlsanity.com/paper/2207.12515/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2022.

## Built on by (most cited first, 30 of 41)

- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations](https://mlsanity.com/paper/2502.13843/) (2025)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [CARTS: Collaborative Agents for Recommendation Textual Summarization](https://mlsanity.com/paper/2506.17765/) (2025)
- [RecPS: Privacy Risk Scoring for Recommender Systems](https://mlsanity.com/paper/2507.18365/) (2025)
- [Counterfactual Collaborative Reasoning](https://mlsanity.com/paper/2307.00165/) (2023)
- [User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations](https://mlsanity.com/paper/2308.00894/) (2023)
- [Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation](https://mlsanity.com/paper/2410.13248/) (2024)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)
- [Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation](https://mlsanity.com/paper/2505.10940/) (2025)
- [Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems](https://mlsanity.com/paper/2511.22515/) (2025)
- [Sell Me This Stock: Unsafe Recommendation Drift in LLM Agents](https://mlsanity.com/paper/2603.12564/) (2026)
- [Fairness-aware Differentially Private Collaborative Filtering](https://mlsanity.com/paper/2303.09527/) (2023)
- [Defense Against Model Extraction Attacks on Recommender Systems](https://mlsanity.com/paper/2310.16335/) (2023)
- [Review of Explainable Graph-Based Recommender Systems](https://mlsanity.com/paper/2408.00166/) (2024)
- [From Aggregation to Selection: User-Validated Distributed Social Recommendation](https://mlsanity.com/paper/2505.21388/) (2025)
- [MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations](https://mlsanity.com/paper/2604.20848/) (2026)
- [Trustworthy Large Models in Vision: A Survey](https://mlsanity.com/paper/2311.09680/) (2023)
- [Securing Recommender System via Cooperative Training](https://mlsanity.com/paper/2401.12700/) (2024)
- [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)

## Abstract (arXiv)

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user-controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.