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

• URL: http://arxiv.org/abs/2308.03333v2
• Date: Fri, 18 Aug 2023 07:05:10 GMT
• Title: Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
• Authors: Bin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, Wei Lin
• Abstract summary: We propose a novel approach for personalized recommendation via Large Language Model (LLM) Our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.
• Score: 18.138629220610678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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