  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.
