Large language models (LLMs) have achieved remarkable progress in the field of natural language processing (NLP), demonstrating remarkable abilities in producing text that resembles human language for various tasks. This opens up new opportunities for employing them in recommender systems (RSs). In this paper, we specifically examine the sample efficiency of LLM-enhanced recommender systems, which pertains to the model's capacity to attain superior performance with a limited quantity of training data. Conventional recommendation models (CRMs) often need a large amount of training data because of the sparsity of features and interactions. Hence, we propose and verify our core viewpoint: Large Language Models Make Sample-Efficient Recommender Systems. We propose a simple yet effective framework (i.e., Laser) to validate the viewpoint from two aspects: (1) LLMs themselves are sample-efficient recommenders; and (2) LLMs, as feature generators and encoders, make CRMs more sample-efficient. Extensive experiments on two public datasets show that Laser requires only a small fraction of training samples to match or even surpass CRMs that are trained on the entire training set, demonstrating superior sample efficiency.

翻译：大语言模型（LLMs）在自然语言处理（NLP）领域取得了显著进展，展现出在为各类任务生成类人文本方面的卓越能力。这为将其应用于推荐系统（RSs）开辟了新的机遇。本文专门研究了LLM增强推荐系统的样本效率，即模型在有限训练数据量下获得优越性能的能力。传统推荐模型（CRMs）由于特征和交互的稀疏性，通常需要大量训练数据。因此，我们提出并验证了我们的核心观点：大语言模型构建样本高效推荐系统。我们提出了一个简单而有效的框架（即Laser），从两个方面验证该观点：（1）LLMs本身即是样本高效的推荐器；（2）LLMs作为特征生成器和编码器，使CRMs更具样本效率。在两个公开数据集上的大量实验表明，Laser仅需一小部分训练样本即可匹配甚至超越在整个训练集上训练的CRMs，展现了卓越的样本效率。