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 a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.

翻译：随着大语言模型在自然语言处理领域取得显著突破，基于大语言模型增强的推荐系统近期备受关注并得到积极探索。本文聚焦于适配并增强纯大语言模型在零样本与少样本推荐任务中的表现。首先，我们识别并形式化了大语言模型在推荐领域的终身序列行为理解不足问题——即即便上下文长度远未触及大语言模型的上下文限制，模型仍难以从长用户行为序列的文本上下文中提取有用信息。为解决该问题并提升大语言模型的推荐性能，我们提出新型框架——检索增强大语言模型（ReLLa），适用于零样本与少样本推荐场景。对于零样本推荐，我们通过语义用户行为检索（SUBR）提升测试样本的数据质量，显著降低大语言模型从用户行为序列中提取关键信息的难度。针对少样本推荐，我们进一步设计检索增强指令微调（ReiT），将SUBR作为训练样本的数据增强技术。具体而言，我们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。通过在真实公共数据集（即MovieLens-1M）上的大量实验，我们证明了ReLLa相较于现有基线模型的优越性，及其在终身序列行为理解中的能力。