The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. This marks a fundamental paradigm shift in the realm of recommendations. Amidst the dynamic research landscape, researchers actively harness the language comprehension and generation capabilities of LLMs to redefine the foundations of recommendation tasks. The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across diverse domains, adoption of unified approaches, holistic learning strategies leveraging shared data reservoirs, transparent decision-making, and iterative improvements. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations, necessitating continuous refinement and evolution in LLM-driven recommender systems.

大规模语言模型（LLMs）在重新塑造推荐系统中的重要性得到强调，将其价值归因于传统推荐系统中缺乏的独特推理能力。与缺乏直接用户交互数据的传统系统不同，LLMs 在推荐物品方面表现出卓越的熟练度，展示了其理解语言细微差别的能力，这标志着推荐领域的一个基本范式转变。在充满活力的研究领域中，研究人员积极利用LLMs的语言理解和生成能力重新定义推荐任务的基础。本文详细探讨了LLMs在推荐框架中的固有优势，包括细微的语境理解、在不同领域之间无缝切换、采用统一的方法、利用共享数据库的全面学习策略、透明的决策制定和迭代改进。尽管具有改变潜力，但仍存在挑战，包括对输入提示的敏感性、偶尔的误解以及意外的推荐，这需要对LLM驱动的推荐系统进行持续的完善和演进。

探究大型语言模型对推荐系统的影响：一次广泛的综述