Handling information loss of graph convolutional networks in collaborative filtering

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to f...

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Vydáno v:Information systems (Oxford) Ročník 109; s. 102051
Hlavní autoři: Xiong, Xin, Li, XunKai, Hu, YouPeng, Wu, YiXuan, Yin, Jian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2022
Témata:
Collaborative filtering
Graph convolutional network
Recommender system
Variational autoencoder
Variational autoencoder
Graph convolutional network
Collaborative filtering
Recommender system
ISSN:0306-4379, 1873-6076
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Shrnutí:Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to fit the graph convolution kernel. And the AE-based CF methods obtain the prediction results by reconstructing the user-item interaction matrix, which does not conduct deep excavation of the behavior patterns, resulting in the limited-expression ability. To solve the above problems, we propose Variational AutoEncoder-Enhanced Graph Convolutional Network (VE-GCN) for CF. Specifically, we use a variational autoencoder (VAE) to compress the interactive behavior patterns as the prior information of GCN to achieve sufficient learning, thus alleviating the information lossy initialization problem. And then the generalized graph Laplacian convolution kernel is proposed in GCN to handle the high-frequency information loss problem caused by Chebyshev Polynomial fitting in the GCN-based CF. To the best of our knowledge, VE-GCN is a feasible method to handle the information loss problems mentioned above in GCN-based CF for the first time. Meanwhile, the structure of GCN is optimized by removing redundant feature transformation and nonlinear activation function, and using DenseGCN to complete multi-level information interaction. Experiments on four real-world datasets show that the VE-GCN achieves state-of-the-art performance. •The information loss in Graph Convolutional Network-based Collaborative Filtering.•The enhanced Graph Convolutional Network.•The optimized Laplacian convolution kernel.•The optimized information propagation framework of Graph Convolutional Network.
ISSN:0306-4379
1873-6076
DOI:10.1016/j.is.2022.102051