A Weighted Preference Optimization Service Recommendation Method Based on Knowledge Graph and Large Language Model

Knowledge graph (KG)-based service recommendation methods address issues such as data sparsity and cold start in real-world service recommendations by integrating external knowledge as auxiliary information. Recently, large language models (LLMs) have gained significant attention due to their powerful comprehension and reasoning capabilities. LLM-based recommendation systems also demonstrate advantages in interpretability and few-shot service reasoning. However, the integration of LLMs and KGs into existing service recommendation methods presents two major challenges: (1) the difficulty of aligning service recommendation tasks with language modeling tasks, and (2) the lack of interpretable quantification of the relationship between knowledge and personalized preferences. To address these challenges, this paper proposes WPKL (Weighted Preference Optimization based on KG and LLM). WPKL leverages external knowledge to assist LLMs in modeling user preferences and employs a hybrid graph neural network (GNN) framework to enhance preference representation. Additionally, a weighted preference optimization (WPO) approach is proposed to fine-tune the LLM, enabling interpretable quantification of user preferences and personalized knowledge. Extensive experimental results demonstrate that WPKL achieves high-quality service recommendations.