PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909084437970944 |
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| author | Qin, Yifang Ju, Wei Luo, Xiao Gu, Yiyang Xiao, Zhiping Zhang, Ming |
| author_facet | Qin, Yifang Ju, Wei Luo, Xiao Gu, Yiyang Xiao, Zhiping Zhang, Ming |
| contents | 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 current state-of-the-art CF methods. Moreover, comprehensive studies empirically validate each component's efficacy in the proposed PolyCF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering Qin, Yifang Ju, Wei Luo, Xiao Gu, Yiyang Xiao, Zhiping Zhang, Ming Information Retrieval 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 current state-of-the-art CF methods. Moreover, comprehensive studies empirically validate each component's efficacy in the proposed PolyCF. |
| title | PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2401.12590 |