The utilization of semantic information is an important research problem in the field of recommender systems, which aims to complement the missing parts of mainstream ID-based approaches. With the rise of LLM, its ability to act as a knowledge base and its reasoning capability have opened up new possibilities for this research area, making LLM-based recommendation an emerging research direction. However, directly using LLM to process semantic information for recommendation scenarios is unreliable and sub-optimal due to several problems such as hallucination. A promising way to cope with this is to use external knowledge to aid LLM in generating truthful and usable text. Inspired by the above motivation, we propose a Knowledge-Enhanced LLMRec method. In addition to using external knowledge in prompts, the proposed method also includes a knowledge-based contrastive learning scheme for training. Experiments on public datasets and in-enterprise datasets validate the effectiveness of the proposed method.

翻译：语义信息的利用是推荐系统领域的一个重要研究问题，旨在补全主流基于ID的方法中缺失的部分。随着大语言模型的兴起，其作为知识库的能力以及推理能力为该研究领域带来了新的可能性，使得基于大语言模型的推荐成为一个新兴的研究方向。然而，直接使用大语言模型处理推荐场景中的语义信息并不可靠且次优，原因包括幻觉等问题。一种有前景的应对方式是借助外部知识辅助大语言模型生成真实且可用的文本。受此动机启发，我们提出了一种知识增强的大语言模型推荐方法。除了在提示中利用外部知识外，该方法还引入了一种基于知识的对比学习方案用于训练。在公开数据集和企业内部数据集上的实验验证了所提出方法的有效性。