DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

• URL: http://arxiv.org/abs/2405.15280v1
• Date: Fri, 24 May 2024 07:07:41 GMT
• Title: DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
• Authors: Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu,
• Abstract summary: We propose a novel model that models positive and negative feedback from a frequency filter perspective. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of the proposed model.
• Score: 51.72177873832969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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