With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on three real-world public datasets to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension. To be highlighted, with only less than 10% training samples, few-shot ReLLa can outperform traditional CTR models that are trained on the entire training set (e.g., DCNv2, DIN, SIM). The code is available \url{https://github.com/LaVieEnRose365/ReLLa}.

翻译：随着大语言模型在自然语言处理领域取得显著突破，大语言模型增强的推荐系统近期受到广泛关注并被积极探索。本文聚焦于适配和增强纯大语言模型以完成零样本和少样本推荐任务。我们首先识别并形式化了大语言模型在推荐领域中的终身序列行为理解不足问题，即大语言模型无法从长用户行为序列的文本上下文中提取有用信息，即使上下文长度远未达到大语言模型的上下文限制。为解决此问题并提升大语言模型的推荐性能，我们提出一种新颖框架——检索增强的大语言模型（ReLLa），适用于零样本和少样本场景下的推荐任务。针对零样本推荐，我们执行语义用户行为检索以提高测试样本的数据质量，大幅降低大语言模型从用户行为序列中提取关键知识的难度。针对少样本推荐，我们进一步设计检索增强指令微调（ReiT），通过将语义用户行为检索作为训练样本的数据增强技术，构建包含原始数据样本及其检索增强对应样本的混合训练数据集。我们在三个真实公开数据集上开展广泛实验，证明ReLLa相较于现有基线模型的优越性及其终身序列行为理解能力。尤其值得注意的是，少样本ReLLa仅需不足10%的训练样本即可超越基于完整训练集训练的经典点击率模型（如DCNv2、DIN、SIM）。代码地址：\url{https://github.com/LaVieEnRose365/ReLLa}。