<p>Recommender systems often face challenges related to information bias and the "filter bubble" effect, which limit content diversity and hinder user satisfaction. While much research has explored the balance between diversity and accuracy, traditional models often overlook the fact that different users have different levels of tolerance for diversity. In this work, we propose a novel approach PDRec-KG, which leverages knowledge graphs to enrich item representations with contextual information and integrate this with users’ historical interactions to estimate their personalized diversity tolerance. Building upon this estimation, we develop a framework that dynamically adjusts the trade-off between diversity and accuracy to align with each user’s preferences. Our method effectively tailors recommendations to individual diversity needs, offering a promising direction for enhancing both the quality and fairness of recommender systems. Comprehensive experiments on real-world datasets demonstrate the effectiveness of our approach. For instance, on the Last.FM dataset, PDRec-KG improves entity coverage by over 24% compared to the strongest baseline, while maintaining competitive accuracy, showcasing its ability to balance the trade-off between diversity and accuracy through personalized diverse recommendations.</p>