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.
View on arXiv@article{zhao2025_2506.05873,
title={ Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks },
author={ Yushang Zhao and Yike Peng and Dannier Li and Yuxin Yang and Chengrui Zhou and Jing Dong },
journal={arXiv preprint arXiv:2506.05873},
year={ 2025 }
}