Recent years have witnessed the wide adoption of large language models (LLM) in different fields, especially natural language processing and computer vision. Such a trend can also be observed in recommender systems (RS). However, most of related work treat 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 the survey can provide the context and guidance needed to explore this interesting and emerging topic.

翻译：近年来，大型语言模型（LLM）已在不同领域得到广泛应用，尤其在自然语言处理和计算机视觉领域。这一趋势在推荐系统（RS）中同样明显。然而，多数相关研究将LLM视为传统推荐流水线的组成部分（例如作为特征提取器），这或许无法充分借助LLM的生成能力。与其将推荐过程分解为分数计算、重排序等多个阶段，不如将其简化为一个单阶段流程：直接从完整的物品池中生成推荐。本综述通过探讨三个问题来审视基于LLM的生成式推荐的进展、方法与未来方向：1）什么是生成式推荐，2）推荐系统为何应迈向生成式推荐，以及3）如何为各类推荐系统任务实现基于LLM的生成式推荐。我们期望本综述能为探索这一有趣且新兴的话题提供背景与指导。