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).

翻译：随着大型语言模型（LLMs）在自然语言处理（NLP）领域取得显著突破，LLM增强的推荐系统备受关注并得到积极探索。本文聚焦于适配并增强纯大型语言模型在零样本与少样本推荐任务中的能力。首先，我们识别并形式化了LLMs在推荐领域的终身序列行为理解不足问题——即即使文本上下文长度远未触及LLMs的上下文限制，LLMs仍无法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LLMs的推荐性能，我们提出一种新颖框架——检索增强大型语言模型（ReLLa），用于零样本与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（SUBR）以提升测试样本的数据质量，显著降低LLMs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），将SUBR作为训练样本的数据增强技术。具体而言，我们构建了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。在三个真实公开数据集上的广泛实验表明，ReLLa在终身序列行为理解能力上优于现有基线模型，且具有显著优势。值得强调的是，少样本ReLLa仅需不到10%的训练样本，即可超越在完整训练集上训练的经典CTR模型（如DCNv2、DIN、SIM）。