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The analysis and mining of user heterogeneous behavior are of paramountimportance in recommendation systems. However, the conventional approach ofincorporating various types of heterogeneous behavior into recommendationmodels leads to feature sparsity and knowledge fragmentation issues. To addressthis challenge, we propose a novel approach for personalized recommendation viaLarge Language Model (LLM), by extracting and fusing heterogeneous knowledgefrom user heterogeneous behavior information. In addition, by combiningheterogeneous knowledge and recommendation tasks, instruction tuning isperformed on LLM for personalized recommendations. The experimental resultsdemonstrate that our method can effectively integrate user heterogeneousbehavior and significantly improve recommendation performance.

Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM