Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.

翻译：大型语言模型（LLM）不仅革新了自然语言处理（NLP）领域，还有潜力重塑许多其他领域，例如推荐系统（RS）。然而，大部分相关工作将LLM视为传统推荐流程的一个组件（如作为特征提取器），这可能无法充分利用LLM的生成能力。不同于将推荐过程分解为多个阶段（例如分数计算和重排序），借助LLM可将此过程简化为单一阶段：直接从完整物品池中生成推荐结果。本综述通过探讨三个问题来回顾基于LLM的生成式推荐的进展、方法及未来方向：1）什么是生成式推荐，2）为何推荐系统应迈向生成式推荐，3）如何为各类推荐任务实现基于LLM的生成式推荐。我们期待本综述能为探索这一有趣且新兴的主题提供必要的背景与指导。