PolyCF: Towards Optimal Spectral Graph Filters for Collaborative Filtering

• DOI: 10.1145/3728464
• Publication Date: 2025-04-07T17:47:11Z
• Authors: Yifang Qin; Wei Ju; Yiyang Gu; Ziyue Qiao; Zhiping Xiao; Ming Zhang

Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achievements of node embedding-based Graph Neural Networks (GNNs), we explore the upper bounds of expressiveness inherent to embedding-based methodologies, and tackle the challenges by reframing the CF task as a graph-signal processing problem. To this end, we propose PolyCF, a flexible graph signal filter that leverages polynomial graph filters to process interaction signals. PolyCF exhibits the capability to capture spectral features across multiple eigenspaces through a series of Generalized Gram filters, and is able to approximate the optimal polynomial response function for recovering missing interactions. A graph optimization objective and a pair-wise ranking objective are jointly used to optimize the parameters of the convolution kernel. Experiments on three widely adopted datasets demonstrate the superiority of PolyCF over the state-of-the-art CF methods.