Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.

翻译：大语言模型（LLM）在包括推荐系统（RS）在内的多个领域具有变革性潜力。已有一些研究专注于利用LLM赋能RS。然而，先前的工作主要集中在将LLM直接用作RS，这可能面临LLM推理成本过高而难以承受的挑战。近期，将LLM集成到RS中，即LLM增强推荐系统（LLMERS），因其在现实应用中解决延迟和内存限制的潜力而引起了广泛关注。本文对旨在利用LLM增强RS能力的最新研究成果进行了全面综述。我们发现该领域出现了一个关键转变，即转向将LLM整合到在线系统中，特别是通过避免在推理阶段使用它们。我们的综述根据被增强的RS模型组件，将现有的LLMERS方法分为三大类：知识增强、交互增强和模型增强。我们对每个类别进行了深入分析，讨论了近期研究的方法、挑战和贡献。此外，我们指出了几个可能进一步推动LLMERS领域发展的有前景的研究方向。