While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metrics remains a significant industry challenge. In line with our initiative to broaden our audience's cultural practices, this study investigates using personalized Determinantal Point Processes (DPPs) to sample diverse and relevant recommendations. We rely on a well-known quality-diversity decomposition of the similarity kernel to give more weight to user preferences. In this paper, we present our implementations of the personalized DPP sampling, evaluate the trade-offs between relevance and diversity through both offline and online metrics, and give insights for practitioners on their use in a production environment. For the sake of reproducibility, we release the full code for our platform and experiments on GitHub.