Low-Pass Graph Convolutional Network for Recommendation
DOI:
https://doi.org/10.1609/aaai.v36i8.20878Keywords:
Machine Learning (ML), Data Mining & Knowledge Management (DMKM)Abstract
Spectral graph convolution is extremely time-consuming for large graphs, thus existing Graph Convolutional Networks (GCNs) reconstruct the kernel by a polynomial, which is (almost) fixed. To extract features from the graph data by learning kernels, Low-pass Collaborative Filter Network (LCFN) was proposed as a new paradigm with trainable kernels. However, there are two demerits of LCFN: (1) The hypergraphs in LCFN are constructed by mining 2-hop connections of the user-item bipartite graph, thus 1-hop connections are not used, resulting in serious information loss. (2) LCFN follows the general network structure of GCNs, which is suboptimal. To address these issues, we utilize the bipartite graph to define the graph space directly and explore the best network structure based on experiments. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model. Codes are available on https://github.com/Wenhui-Yu/LCFN.Downloads
Published
2022-06-28
How to Cite
Yu, W., Zhang, Z., & Qin, Z. (2022). Low-Pass Graph Convolutional Network for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8954-8961. https://doi.org/10.1609/aaai.v36i8.20878
Issue
Section
AAAI Technical Track on Machine Learning III