Bridging Interests and Truth: Towards Mitigating Fake News with Personalized and Truthful Recommendations

While the proliferation of fake news poses a significant threat to information integrity, existing efforts to counter it, especially within personalized news recommendation systems, have proven inadequate.Traditional methods, which often rely on classifiers to filter out fake content, are limited by their accuracy and their inability to fully capture the diverse interests of users.To address these challenges, we proposed PRISM-Protection-enhanced Recommendation with Interest-aware Sequential Modeling-a novel framework based on diffusion models.PRISM harnesses the generative and control capabilities of diffusion models to progressively learn the implicit distribution of user interests from their reading history, thereby generating personalized recommendations that align with both their linguistic preferences and interest domains.Furthermore, PRISM incorporates pre-trained authenticity representations as constraints during content generation, ensuring the credibility of the recommended news and effectively curbing the spread of fake news.Comprehensive evaluations from multiple dimensions demonstrate the superiority of our model.