The recent advancements in the field of recommendation systems have seen a significant shift towards leveraging large language models (LLMs) to enhance personalization and accuracy. Innovations such as the integration of triple-modality data (visual, textual, and graph) with LLMs have shown promise in capturing the multifaceted nature of user behaviors and item features, leading to more comprehensive and accurate recommendations. Additionally, the use of sequential frameworks and co-action graphs has addressed the unique challenges posed by sparse data and diverse user interests in e-commerce platforms. Notably, LLMs have demonstrated substantial improvements in key performance indicators such as precision, recall, and diversity, suggesting a strong potential for enhancing user experience and platform sales. The field is also exploring the application of LLMs in narrative-driven recommenders, where models are tasked with generating contextually relevant suggestions based on free-form user requests, outperforming traditional methods. These developments collectively indicate a move towards more sophisticated and context-aware recommendation systems, driven by the capabilities of LLMs.