Frequency-aware Graph Signal Processing for Collaborative Filtering

• URL: http://arxiv.org/abs/2402.08426v1
• Date: Tue, 13 Feb 2024 12:53:18 GMT
• Title: Frequency-aware Graph Signal Processing for Collaborative Filtering
• Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang and Ning Gu
• Abstract summary: We propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood.
• Score: 26.317108637430664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item characteristics and failed to utilize user and item high-order neighborhood information to model user preference, thus leading to sub-optimal performance. To address the above issues, we propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter that work in a successive manner, to capture both unique and common user/item characteristics to more accurately model user preference. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood, to fully utilize high-order neighborhood information of users/items for more accurate user preference modeling. Finally, we combine these two modules via a linear model to further improve recommendation accuracy. Extensive experiments on six public datasets demonstrate the superiority of our method from the perspectives of prediction accuracy and training efficiency compared with state-of-the-art GCN-based recommendation methods and GSP-based recommendation methods.

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