Towards Mitigating Unwanted Recommendations with Risk-Controlling Recommender Systems

Recommender systems exert a profound influence on digital information consumption. Although they excel at content personalisation, recommenders are increasingly investigated for disseminating irrelevant, unwanted, or harmful suggestions. Such exposure diminishes user satisfaction and exacerbates broader societal challenges, including the spread of misinformation, radicalisation, and the systemic erosion of trust. While existing platforms provide tools to limit the disclosure of problematic content, these mechanisms often lack efficacy and fail to adapt dynamically to user input. This article presents an intuitive, model-agnostic, and distribution-free framework that employs conformal risk control to provide statistical guarantees regarding unwanted content in personalised feeds. By leveraging simple feedback, our method ensures that the risk of encountering undesirable items is provably bounded. Furthermore, we address a common limitation of traditional conformal risk control, whereby risk mitigation often yields overly restrictive or smaller recommendation sets. We overcome this by incorporating implicit feedback from consumed items to expand the set of recommendations while maintaining robust risk constraints. Experimental evaluation conducted on datasets from two different real-world scenarios ( i.e., a short-video platform and a music streaming service) confirms that our approach achieves a controllable and effective reduction of unwanted recommendations in diverse domains with minimal computation overhead.