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arXiv · 2506.05873

Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks

Yushang Zhao·Yike Peng·Dannier Li·Yuxin Yang·Chengrui Zhou·Jing Dong
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Abstract

With the rapid growth of fintech, personalized financial product recommendations have become increasingly important. Traditional methods like collaborative filtering or content-based models often fail to capture users' latent preferences and complex relationships. We propose a hybrid framework integrating large language models (LLMs) and graph neural networks (GNNs). A pre-trained LLM encodes text data (e.g., user reviews) into rich feature vectors, while a heterogeneous user-product graph models interactions and social ties. Through a tailored message-passing mechanism, text and graph information are fused within the GNN to jointly optimize embeddings. Experiments on public and real-world financial datasets show our model outperforms standalone LLM or GNN in accuracy, recall, and NDCG, with strong interpretability. This work offers new insights for personalized financial recommendations and cross-modal fusion in broader recommendation tasks.

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Yushang Zhao, Yike Peng, Dannier Li, Yuxin Yang, Chengrui Zhou, Jing Dong. 2025-06-06. Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks. https://arxiv.org/abs/2506.05873

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