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

• URL: http://arxiv.org/abs/2507.16829v1
• Date: Wed, 09 Jul 2025 21:27:35 GMT
• Title: You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
• Authors: Giovanni De Toni, Erasmo Purificato, Emilia Gómez, Bruno Lepri, Andrea Passerini, Cristian Consonni,
• Abstract summary: This paper introduces an intuitive, model-agnostic, and distribution-free method that uses conformal risk control to provably bound unwanted content in personalized recommendations.<n>Our approach ensures an effective and controllable reduction of unwanted recommendations with minimal effort.
• Score: 17.919072158085754
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System [60.719158008403376]
  Vulnerability-aware Adversarial Training (VAT) is designed to defend against poisoning attacks in recommender systems. VAT employs a novel vulnerability-aware function to estimate users' vulnerability based on the degree to which the system fits them.
  arXiv  Detail & Related papers  (2024-09-26T02:24:03Z)
• Content-Agnostic Moderation for Stance-Neutral Recommendation [13.210645250173997]
  Content-agnostic moderation does not rely on the actual content being moderated, arguably making it less prone to forms of censorship. We introduce two novel content-agnostic moderation methods that modify the recommendations from the content recommender to disperse user-item co-clusters without relying on content features. Our results indicate that achieving stance neutrality without direct content information is not only feasible but can also help in developing more balanced and informative recommendation systems without substantially degrading user engagement.
  arXiv  Detail & Related papers  (2024-05-29T09:50:39Z)
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack [55.24441467292359]
  We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
  arXiv  Detail & Related papers  (2023-12-23T09:44:57Z)
• User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations [96.45414741693119]
  We present a user-controllable recommender system that seamlessly integrates explainability and controllability. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system.
  arXiv  Detail & Related papers  (2023-08-02T01:13:36Z)
• The Amplification Paradox in Recommender Systems [12.723777984461693]
  We show through simulations that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox. Our results call for a nuanced interpretation of algorithmic amplification'' and highlight the importance of modeling the utility of content to users when auditing recommender systems.
  arXiv  Detail & Related papers  (2023-02-22T09:12:48Z)
• Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design [0.0]
  Recommendation systems are vulnerable to malicious user biases, such as fake reviews to promote or demote specific products. Deep-learning collaborative filtering recommendation systems have shown to be more vulnerable to this bias. We discuss the need for designing the robust recommendation system for fairness and stability.
  arXiv  Detail & Related papers  (2022-09-28T13:00:26Z)
• Recommending with Recommendations [1.1602089225841632]
  Recommendation systems often draw upon sensitive user information in making predictions. We show how to address this deficiency by basing a service's recommendation engine upon recommendations from other existing services. In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space.
  arXiv  Detail & Related papers  (2021-12-02T04:30:15Z)
• PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion [58.870444954499014]
  A common practice is to subsume recommender systems under the decentralized federated learning paradigm. We present a systematic approach to backdooring federated recommender systems for targeted item promotion.
  arXiv  Detail & Related papers  (2021-10-21T06:48:35Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Reward Constrained Interactive Recommendation with Natural Language Feedback [158.8095688415973]
  We propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference. Our proposed framework is general and is further extended to the task of constrained text generation.
  arXiv  Detail & Related papers  (2020-05-04T16:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.