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

• URL: http://arxiv.org/abs/2506.05873v1
• Date: Fri, 06 Jun 2025 08:41:33 GMT
• Title: Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks
• Authors: Yushang Zhao, Yike Peng, Dannier Li, Yuxin Yang, Chengrui Zhou, Jing Dong,
• Abstract summary: We propose a hybrid framework integrating large language models (LLMs) and graph neural networks (GNNs)<n>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.<n> Experiments on public and real-world financial datasets show our model outperforms standalone LLM or GNN in accuracy, recall, and NDCG, with strong interpretability.
• Score: 2.86471061970102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• LLM-Augmented Graph Neural Recommenders: Integrating User Reviews [2.087411180679868]
  We propose a framework that employs a Graph Neural Network (GNN)-based model and an large language model (LLMs) to produce review-aware representations.<n>Our approach balances user-item interactions against text-derived features, ensuring that user's both behavioral and linguistic signals are effectively captured.
  arXiv  Detail & Related papers  (2025-04-03T00:40:09Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.<n>Recent research has focused on graph foundation models (GFMs)<n>GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document [7.0421339410165045]
  This study introduces a novel approach to sentence-level relation extraction (RE) It integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. Our experiments, conducted on the CrossRE dataset, demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-10-30T20:48:34Z)
• Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations [13.878297630442674]
  This paper introduces a novel framework for multi-behavior recommendations, leveraging the fusion of triple-modality.<n>Our proposed model called Triple Modality Fusion (TMF) utilizes the power of large language models (LLMs) to align and integrate these three modalities.<n>Extensive experiments demonstrate the effectiveness of our approach in improving recommendation accuracy.
  arXiv  Detail & Related papers  (2024-10-16T04:44:15Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Towards Graph Foundation Models for Personalization [9.405827216171629]
  We present a graph-based foundation modeling approach tailored to personalization. Our approach has been rigorously tested and proven effective in delivering recommendations across a diverse array of products.
  arXiv  Detail & Related papers  (2024-03-12T10:12:59Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.