Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks, such as personalized recommendation, rely on task-related information for optimal performance. Current methods attempt to supplement task-related information to LLMs by designing appropriate prompts or employing supervised fine-tuning techniques. Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models. On the other hand, domain-specific models for personalized recommendation which mainly rely on user interactions are susceptible to data sparsity due to their limited common knowledge capabilities. To address these issues, we proposes a method to bridge the information gap between the domain-specific models and the general large language models. Specifically, we propose an information sharing module which serves as an information storage mechanism and also acts as a bridge for collaborative training between the LLMs and domain-specific models. By doing so, we can improve the performance of LLM-based recommendation with the help of user behavior pattern information mined by domain-specific models. On the other hand, the recommendation performance of domain-specific models can also be improved with the help of common knowledge learned by LLMs. Experimental results on three real-world datasets have demonstrated the effectiveness of the proposed method.

翻译：生成式大语言模型（LLMs）擅长解决通用问题，但在处理领域特定任务时往往力不从心。这是因为大多数领域特定任务，如个性化推荐，需要依赖任务相关信息才能达到最优性能。当前方法试图通过设计合适的提示或采用监督微调技术向LLMs补充任务相关信息。然而，这些方法面临一个关键问题：推荐系统中社区行为模式等信息难以用自然语言表达，这限制了LLMs超越最先进领域专用模型的能力。另一方面，主要依赖用户交互的个性化推荐领域专用模型，由于其常识推理能力有限，容易受到数据稀疏性的影响。为解决这些问题，我们提出了一种弥合领域专用模型与通用大语言模型之间信息鸿沟的方法。具体而言，我们设计了一个信息共享模块，该模块既作为信息存储机制，又充当LLMs与领域专用模型协同训练的桥梁。通过这种方式，我们能够借助领域专用模型挖掘的用户行为模式信息提升基于LLM的推荐性能；同时，利用LLMs习得的常识知识也能改善领域专用模型的推荐效果。在三个真实数据集上的实验结果证明了所提方法的有效性。