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

TL;DR

This paper introduces a hybrid approach combining large language models and graph neural networks to improve personalized financial product recommendations, demonstrating superior accuracy and interpretability over traditional methods.

Contribution

It presents a novel framework that fuses LLMs and GNNs for enhanced recommendation quality and interpretability in fintech applications.

Findings

Outperforms standalone LLM or GNN models in accuracy, recall, and NDCG.

Effectively captures complex user preferences and social ties.

Provides interpretable recommendations through cross-modal fusion.

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.