The recent advancements in the field of multimodal AI and explainable recommender systems have shown significant promise in enhancing transparency and user trust. Researchers are increasingly leveraging Large Language Models (LLMs) to generate explanations for recommendations, thereby improving the interpretability of recommender systems. This shift towards explainable AI is crucial for fostering transparency and ensuring user trust in AI-driven recommendations. Additionally, the integration of LLMs with product knowledge graphs in e-commerce is demonstrating improved user engagement and transaction rates, highlighting the practical applications of these models. The development of efficient explainability frameworks for multimodal generative models is also making strides, reducing computational costs and memory footprints, which is essential for deploying these models in real-world scenarios. Notably, the use of LLMs in CTR prediction is being explored to enhance both recommendation accuracy and interpretability, addressing the limitations of traditional post-hoc explanation methods. Overall, the field is moving towards more transparent, efficient, and user-centric AI solutions, with a strong focus on leveraging LLMs for explainability and interpretability.