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An improved recommendation based on graph convolutional network

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Abstract

Graph convolutional network is a recently developed artificial neural network method commonly used in recommendation system research. This paper points out three shortcomings of existing recommendation systems based on the graph convolutional network. 1. Existing models that take the one-hot encoding based on node ordinal numbers in the graph or encoding based on original entity attributes as input may not fully utilize the information carried by the attribute interactions. 2. Previous models update the node embeddings only by the first-order neighbors in the graph convolution layer, which is easily affected by noise. 3. Existing models do not take into account differences in user opinions. We propose an improved graph convolutional network-based collaborative filtering model to address these drawbacks. We identify inner and cross interaction between user attributes and item attributes, and then we take the vector representations of aggregated attributes graph as input. In the convolutional layer, we aggregate the second-order collaborative signals and incorporate the different user opinions. The experiments on three public datasets show that our model outperforms state-of-the-art models.

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Data Availability

Data and Codes are available uponrequest.

Notes

  1. https://github.com/hexiangnan/neural_factorization_machine

  2. https://github.com/riannevdberg/gc-mc

  3. https://github.com/wenqifan03/GraphRec-WWW19

  4. https://github.com/xiangwang1223/neural_graph_collaborative_ltering

  5. https://github.com/kuandeng/LightGCN

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Funding

This work was financially supported by the Philosophy and Social Science Planning Project of Guangdong (GD19CGL34), the Guangdong Computational Science Key Laboratory Development Fund Project (2021010), the National Statistical Science Research Project (2020LY018), the National Social Science Foundation of China (18ZDA093), and Natural Science Foundation of Guangdong (2022A1515011489).

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Authors and Affiliations

  1. School of Mathematics and Information, South China Agricultural University, Tianhe, Guangzhou, 510642, Guangdong, China

    Yichen He, Yijun Mao & Wanrong Gu

  2. School of Economy, Jinan University, Tianhe, Guangzhou, 510632, Guangdong, China

    Xianfen Xie

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  1. Yichen He
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  2. Yijun Mao
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  3. Xianfen Xie
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  4. Wanrong Gu
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Contributions

Yichen He and Yijun Mao wrote the main manuscript text. Yichen He and Wanrong Gu wrote the key code and conducted the experiments. Xianfen Xie conceived and designed the analysis. All authors reviewed the manuscript.

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Correspondence to Wanrong Gu.

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He, Y., Mao, Y., Xie, X. et al. An improved recommendation based on graph convolutional network. J Intell Inf Syst 59, 801–823 (2022). https://doi.org/10.1007/s10844-022-00727-3

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