Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915330907963392 |
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| author | Zhao, Yushang Peng, Yike Li, Dannier Yang, Yuxin Zhou, Chengrui Dong, Jing |
| author_facet | Zhao, Yushang Peng, Yike Li, Dannier Yang, Yuxin Zhou, Chengrui Dong, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks Zhao, Yushang Peng, Yike Li, Dannier Yang, Yuxin Zhou, Chengrui Dong, Jing Information Retrieval Artificial Intelligence 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. |
| title | Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05873 |