The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current approaches that integrate LLMs into RSs solely utilize either LLM or conventional recommender model (CRM) to generate final recommendations, without considering which data segments LLM or CRM excel in. To fill in this gap, we conduct experiments on MovieLens-1M and Amazon-Books datasets, and compare the performance of a representative CRM (DCNv2) and an LLM (LLaMA2-7B) on various groups of data samples. Our findings reveal that LLMs excel in data segments where CRMs exhibit lower confidence and precision, while samples where CRM excels are relatively challenging for LLM, requiring substantial training data and a long training time for comparable performance. This suggests potential synergies in the combination between LLM and CRM. Motivated by these insights, we propose Collaborative Recommendation with conventional Recommender and Large Language Model (dubbed \textit{CoReLLa}). In this framework, we first jointly train LLM and CRM and address the issue of decision boundary shifts through alignment loss. Then, the resource-efficient CRM, with a shorter inference time, handles simple and moderate samples, while LLM processes the small subset of challenging samples for CRM. Our experimental results demonstrate that CoReLLa outperforms state-of-the-art CRM and LLM methods significantly, underscoring its effectiveness in recommendation tasks.

翻译：大型语言模型（LLM）的兴起通过增强用户行为建模和内容理解，为推荐系统（RS）带来了新的机遇。然而，当前将LLM集成到RS中的方法仅单独利用LLM或传统推荐模型（CRM）生成最终推荐，未考虑LLM或CRM各自擅长处理的数据片段。为填补这一空白，我们在MovieLens-1M和Amazon-Books数据集上进行实验，比较了代表性CRM（DCNv2）和LLM（LLaMA2-7B）在不同数据样本组上的性能。研究结果揭示，LLM在CRM表现出较低置信度和精度的数据片段中表现优异，而CRM擅长的样本对LLM而言相对困难，需要大量训练数据和较长训练时间才能达到可比性能。这表明LLM与CRM之间存在潜在的协同效应。受此启发，我们提出传统推荐与大语言模型协同推荐框架（简称\textit{CoReLLa}）。在该框架中，我们首先联合训练LLM和CRM，并通过对齐损失解决决策边界偏移问题。随后，资源效率更高、推理时间更短的CRM处理简单和中等难度的样本，而LLM则处理CRM难以应对的小部分困难样本。实验结果表明，CoReLLa显著优于最先进的CRM和LLM方法，凸显其在推荐任务中的有效性。