DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
<|reference_start|>DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback: The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike, low rating) that widely exist in real-world recommender systems. How to utilize negative feedback in graph-based recommendations still remains underexplored. In this study, we first conducted a comprehensive experimental analysis and found that (1) existing graph neural networks are not well-suited for modeling negative feedback, which acts as a high-frequency signal in a user-item graph. (2) The graph-based recommendation suffers from the representation degeneration problem. Based on the two observations, we propose a novel model that models positive and negative feedback from a frequency filter perspective called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN). Specifically, in DFGNN, the designed dual-frequency graph filter (DGF) captures both low-frequency and high-frequency signals that contain positive and negative feedback. Furthermore, the proposed signed graph regularization is applied to maintain the user/item embedding uniform in the embedding space to alleviate the representation degeneration problem. Additionally, we conduct extensive experiments on real-world datasets and demonstrate the effectiveness of the proposed model. Codes of our model will be released upon acceptance.<|reference_end|>
@article{wu2024dfgnn:,
    title={DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback},
    author={Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin,
  Zhanhui Kang, Yongjun Xu},
    journal={arXiv preprint arXiv:2405.15280},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15280},
    primaryClass={cs.IR cs.AI cs.LG}
}