Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.

翻译：大语言模型（LLMs）已在多个领域展现出卓越性能，促使研究者探索其在推荐系统中的潜力。初步尝试利用LLMs的丰富知识及通过上下文学习实现的强泛化能力等优势，将推荐任务构建为提示形式。然而，由于LLMs的训练任务与推荐任务之间存在显著差异，且预训练阶段缺乏充分的推荐数据，LLMs在推荐任务中的表现仍不理想。为弥合这一差距，我们考虑通过使用推荐数据调优LLM，构建大型推荐语言模型。为此，我们提出了一种高效且有效的调优框架，用于将LLMs与推荐系统对齐，即TALLRec。我们证明，所提出的TALLRec框架在电影和图书领域能够显著增强LLMs的推荐能力，即使使用少于100个样本的有限数据集。此外，该框架效率极高，可在搭载LLaMA-7B的单个RTX 3090上运行。更进一步，经微调的LLM展现出稳健的跨领域泛化能力。我们的代码和数据已开源在https://github.com/SAI990323/TALLRec。