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.

翻译：用户异构行为的分析与挖掘在推荐系统中至关重要。然而，将多种异构行为类型引入推荐模型的传统方法会导致特征稀疏性和知识碎片化问题。为应对这一挑战，我们提出一种基于大语言模型的个性化推荐新方法，通过从用户异构行为信息中提取并融合异构知识。此外，通过将异构知识与推荐任务相结合，对大语言模型进行指令微调以实现个性化推荐。实验结果表明，我们的方法能够有效整合用户异构行为，并显著提升推荐性能。