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.
View on arXiv@article{kanezashi2025_2504.02195,
title={ LLM-Augmented Graph Neural Recommenders: Integrating User Reviews },
author={ Hiroki Kanezashi and Toyotaro Suzumura and Cade Reid and Md Mostafizur Rahman and Yu Hirate },
journal={arXiv preprint arXiv:2504.02195},
year={ 2025 }
}