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Large Language Models (LLMs) have demonstrated remarkable performance acrossdiverse domains, thereby prompting researchers to explore their potential foruse in recommendation systems. Initial attempts have leveraged the exceptionalcapabilities of LLMs, such as rich knowledge and strong generalization throughIn-context Learning, which involves phrasing the recommendation task asprompts. Nevertheless, the performance of LLMs in recommendation tasks remainssuboptimal due to a substantial disparity between the training tasks for LLMsand recommendation tasks, as well as inadequate recommendation data duringpre-training. To bridge the gap, we consider building a Large RecommendationLanguage Model by tunning LLMs with recommendation data. To this end, wepropose an efficient and effective Tuning framework for Aligning LLMs withRecommendation, namely TALLRec. We have demonstrated that the proposed TALLRecframework can significantly enhance the recommendation capabilities of LLMs inthe movie and book domains, even with a limited dataset of fewer than 100samples. Additionally, the proposed framework is highly efficient and can beexecuted on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLMexhibits robust cross-domain generalization. Our code and data are available athttps://github.com/SAI990323/TALLRec.

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation