Measuring, Revealing and Mitigating Harmful Content Propagation in Recommender Systems

Recommender systems help users navigate vast amounts of information by filtering content according to their historical behaviors. As these systems increasingly shape everyday information access, they largely determine the content users are exposed to. However, current research typically prioritizes catering to user interest, while overlooking the existence and propagation of harmful content, such as violence, hate speech, and pornography. In this work, we take a systematic step toward investigating harmful content in recommender systems from three aspects: measuring, revealing, and mitigating. First, we construct datasets with harmfulness-related annotations and present new evaluation metrics to quantitatively measure the harmfulness of recommendation results. Second, our empirical analysis reveals that users tend to engage with harmful content and prevailing recommendation methods often contribute to the proliferation of such content. Third, to address this issue, we introduce a generic framework, MitiHarm, which integrates harmfulness-related signals into both item and user representation learning. Experiments show that MitiHarm can significantly mitigate the propagation of harmful content while preserving user satisfaction. This study highlights the potential risks of modern recommender systems and establishes a foundation for developing more responsible approaches¹.