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Degree-aware embedding-based multi-correlated graph convolutional collaborative filtering

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Abstract

In light of the remarkable capacity of graph convolutional network (GCN) in representation learning, researchers have incorporated it into collaborative filtering recommendation systems to capture high-order collaborative signals. However, existing GCN-based collaborative filtering models still exhibit three deficiencies: the failure to consider differences between users’ activity and preferences for items’ popularity, the low-order feature information of users and items has been inadequately employed, and neglecting the correlated relationships among isomorphic nodes. To address these shortcomings, this paper proposes a degree-aware embedding-based multi-correlated graph convolutional collaborative filtering (Da-MCGCF). Firstly, Da-MCGCF combines users’ activity and preferences for items’ popularity to perform neighborhood aggregation in the user-item bipartite graph, thereby generating more precise representations of users and items. Secondly, Da-MCGCF employs a low-order feature fusion strategy to integrate low-order features into the process of mining high-order features, which enhances feature representation capabilities, and enables the exploration of deeper relationships. Furthermore, we construct two isomorphic graphs by employing an adaptive approach to explore correlated relationships at the isomorphic level between users and items. Subsequently, we aggregate the features of isomorphic users and items separately to complement their representations. Finally, we conducted extensive experiments on four public datasets, thereby validating the effectiveness of our proposed model.

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Data availability

Ciao dataset is available at http://www.ciao.co.uk, Yelp dataset is available at https://www.yelp.com/dataset, ML-1 M dataset is available at https://github.com/familyld/DeepCF and Gowalla dataset is available at https://snap.stanford.edu/data/loc-gowalla.html.

Code availability

The source code of Da-MCGCF will be published at https://github.com/xiaoma012/Da-MCGCF after paper acceptance for publication.

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Funding

This work was supported by the Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region under Grant No.2021D01E14.

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Chao Ma designed and performed the experiments, formal analysis, and writing. Jiwei Qin reviewing and editing manuscript. Tao Wang and Aohua Gao give advice. All authors reviewed the Manuscript.

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Correspondence to Jiwei Qin.

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Ma, C., Qin, J., Wang, T. et al. Degree-aware embedding-based multi-correlated graph convolutional collaborative filtering. J Supercomput 80, 25911–25932 (2024). https://doi.org/10.1007/s11227-024-06354-9

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