Imagine your favorite streaming service not only suggesting movies you might like but also explaining *why* it thinks you'll enjoy them. This is the promise of Large Language Models (LLMs) in recommender systems. A recent wave of research explores how LLMs like ChatGPT and LLaMA can transform the way recommendations are explained, making them more personalized, transparent, and engaging. Instead of just seeing a list of movies, you might receive a narrative like, "Based on your love for sci-fi epics and strong female leads, we think you'll be captivated by this film's unique blend of space opera and political intrigue." This shift from technical explanations to human-centric justifications represents a significant leap in user experience. Studies show users prefer these LLM-generated explanations for their detail, creativity, and ability to provide contextual background, fostering greater trust. However, researchers are also grappling with challenges. While LLMs excel at generating engaging narratives, maintaining clarity and conciseness, especially in complex recommendations, is crucial. Overly detailed explanations can overwhelm users. Furthermore, there's a distinction between providing a justification and a true explanation. LLMs often provide the *why* from a user's perspective, but not the *how* of the underlying algorithms, potentially hindering deeper understanding for some users. The future likely lies in hybrid approaches that combine the engaging storytelling of LLMs with the transparency of traditional explanation frameworks, providing nuanced insights for both casual users and those seeking a more technical understanding. As researchers continue to refine these approaches, we can expect recommender systems to become even more personalized, intuitive, and integrated into our digital lives.