Contrastive Learning-optimized recommendation data to Construct a Language Model Recommendation

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial at-tempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong gener-alization through In-context Learning, which involves phrasing the recommendation task as prompts.Nevertheless, Given the significant disparity between the training tasks of LLMs and recom-mendation tasks, along with the data sparsity issue arising from insufficient recommendation data during pre-training, the performance of LLMs in recommendation tasks remains unsatisfactory. To bridge this gap, we propose fine-tuning LLMs with Contrastive Learning-optimized recommendation data to Construct a Language Model Recommendation. namely CLCLMRec. The CLCLMRec frame-work aims to achieve an effective and efficient integration of Large Language Models with recommen-dation systems in settings with low GPU memory consumption. The framework encompasses two cru-cial tuning phases: firstly, instruction tuning, a universal LLM training process designed to enhance the generalization capabilities of LLMs; and secondly, recommendation tuning, which focuses on fine-tuning the model specifically for recommendation tasks. In Rec-tuning, we employ the self-attention mechanism to process embedded data and emphasize sequential recommendation methods to capture users' dynamic interests. However, addressing the challenge of data sparsity, we innovatively intro-duce contrastive learning, extracting self-supervised signals from user behavior sequences to train large models more effectively and uncover meaningful user patterns. Ultimately, we fuse "recommen-dation instructions" with "recommendation inputs" to construct "instruction inputs" and utilize "rec-ommendation outputs" as "instruction outputs" for training, thereby building a Large Recommendation Language Model (LRLM). Experimental results demonstrate that even with extremely limited data (fewer than 100 samples), the CLCLMRec framework can significantly boost the recommendation ca-pabilities of LLMs in domains such as movies and books.