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.

利用外部知识辅助大规模语言模型（LLM）在生成真实可用的文本方面具有潜力，因此我们提出了一种名为‘Knowledge-Enhanced LLMRec’的方法，通过使用外部知识在生成过程中，并且通过基于知识的对比学习方案来训练模型，实验证实了该方法的有效性。