LLM-Augmented Graph Neural Recommenders: Integrating User Reviews

• URL: http://arxiv.org/abs/2504.02195v1
• Date: Thu, 03 Apr 2025 00:40:09 GMT
• Title: LLM-Augmented Graph Neural Recommenders: Integrating User Reviews
• Authors: Hiroki Kanezashi, Toyotaro Suzumura, Cade Reid, Md Mostafizur Rahman, Yu Hirate,
• Abstract summary: 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.
• Score: 2.087411180679868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems increasingly aim to combine signals from both user reviews and purchase (or other interaction) behaviors. While user-written comments provide explicit insights about preferences, merging these textual representations from large language models (LLMs) with graph-based embeddings of user actions remains a challenging task. In this work, we propose a framework that employs both a Graph Neural Network (GNN)-based model and an LLM to produce review-aware representations, preserving review semantics while mitigating textual noise. Our approach utilizes a hybrid objective that balances user-item interactions against text-derived features, ensuring that user's both behavioral and linguistic signals are effectively captured. We evaluate this method on multiple datasets from diverse application domains, demonstrating consistent improvements over a baseline GNN-based recommender model. Notably, our model achieves significant gains in recommendation accuracy when review data is sparse or unevenly distributed. These findings highlight the importance of integrating LLM-driven textual feedback with GNN-derived user behavioral patterns to develop robust, context-aware recommender systems.

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