融合大模型与图神经网络，提升金融产品推荐精准度。
Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks
- 用大模型提取文本特征，图网络建模用户-产品关系
- 在多个数据集上准确率、召回率、NDCG均优于单一模型
- 适合对推荐可解释性有要求的金融科技场景
随着金融科技快速发展，个性化金融产品推荐日益重要。传统协同过滤或基于内容的方法难以捕捉用户潜在偏好和复杂关系。本文提出一种融合大型语言模型（LLMs）与图神经网络（GNNs）的混合框架：预训练大模型将用户评论等文本数据编码为丰富特征向量，异构用户-产品图建模交互与社交关系；通过定制的消息传递机制，在GNN中融合文本与图信息，联合优化嵌入表示。在公开及真实金融数据集上的实验表明，该模型在准确率、召回率和NDCG指标上均优于单独使用大模型或图神经网络，且具备较强可解释性。本工作为个性化金融推荐及更广泛的跨模态融合任务提供了新思路。
原文摘要 · Abstract (English)
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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