mlsanityData to 14 Oct 2026

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control

arXiv 2507.16829 ↗First posted 9 Jul 2025cs.IR cs.AIcited by 3

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Abstract (arXiv)

Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insufficiently effective and slow to adapt to users' feedback. This paper introduces an intuitive, model-agnostic, and distribution-free method that uses conformal risk control to provably bound unwanted content in personalized recommendations by leveraging simple binary feedback on items. We also address a limitation of traditional conformal risk control approaches, i.e., the fact that the recommender can provide a smaller set of recommended items, by leveraging implicit feedback on consumed items to expand the recommendation set while ensuring robust risk mitigation. Our experimental evaluation on data coming from a popular online video-sharing platform demonstrates that our approach ensures an effective and controllable reduction of unwanted recommendations with minimal effort. The source code is available here: https://github.com/geektoni/mitigating-harm-recsys.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 76 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

34 of 76 references.

References · 34

  1. 2017Neural Collaborative Filteringcited by 1,111
  2. 2022Conformal Risk Controlcited by 299
  3. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 288
  4. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
  5. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  6. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  7. 2022KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videoscited by 72
  8. 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
  9. 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 68
  10. 2022Improving Expert Predictions with Conformal Predictioncited by 54
  11. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 53
  12. 2018RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendationcited by 51
22 more
  1. 2022A Survey on Trustworthy Recommender Systemscited by 41
  2. 2023Graph Transformer for Recommendationcited by 28
  3. 2023Designing Decision Support Systems Using Counterfactual Prediction Setscited by 26
  4. 2024Towards Human-AI Complementarity with Prediction Setscited by 25
  5. 2021User Tampering in Reinforcement Learning Recommender Systemscited by 23
  6. 2022PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagementcited by 21
  7. 2021The Effect of People Recommenders on Echo Chambers and Polarizationcited by 20
  8. 2018The Music Streaming Sessions Datasetcited by 18
  9. 2022An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changescited by 15
  10. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  11. 2020Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendationcited by 12
  12. 2024SIGformer: Sign-aware Graph Transformer for Recommendationcited by 12
  13. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  14. 2021SiReN: Sign-Aware Recommendation Using Graph Neural Networkscited by 9
  15. 2022Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysiscited by 6
  16. 2022Recommender Systems and Algorithmic Hatecited by 5
  17. 2024Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systemscited by 5
  18. 2024ReCODE: Modeling Repeat Consumption with Neural ODEcited by 5
  19. 2024Harm Mitigation in Recommender Systems under User Preference Dynamicscited by 5
  20. 2024A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendationcited by 4
  21. 2023How to Train Your YouTube Recommender to Avoid Unwanted Videoscited by 2
  22. 2024Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
  2. 2026Gaming the Metric, Not the Harm: Certifying Safety Audits against Strategic Platform Manipulationcited by 0
  3. 2026GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendationcited by 0