KELLMRec: Knowledge-Enhanced Large Language Models for Recommendation
Weiqing Luo Chonggang Song Lingling Yi Gong Cheng
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.