The analysis and mining of user heterogeneous behavior are of paramount
importance in recommendation systems. However, the conventional approach of
incorporating various types of heterogeneous behavior into recommendation
models leads to feature sparsity and knowledge fragmentation issues. To address
this challenge, we propose a novel approach for personalized recommendation via
Large Language Model (LLM), by extracting and fusing heterogeneous knowledge
from user heterogeneous behavior information. In addition, by combining
heterogeneous knowledge and recommendation tasks, instruction tuning is
performed on LLM for personalized recommendations. The experimental results
demonstrate that our method can effectively integrate user heterogeneous
behavior and significantly improve recommendation performance.

通过从用户的异构行为信息中提取和融合异构知识，借助大型语言模型（LLM）实现个性化推荐，并通过结合异构知识与推荐任务，在 LLM 上进行指令调整以显著提高推荐性能。实验结果表明，我们的方法能够有效整合用户的异构行为并显著改善推荐性能。