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CoGCN: co-occurring item-aware GCN for recommendation

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
  • Published:
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Abstract

Graph convolution networks (GCNs) play an increasingly vital role in recommender systems, due to their remarkable relation modeling and representation capabilities. Concretely, they can capture high-order semantic correlations within sparse bipartite interaction graphs, thereby enhancing user–item collaborative encodings. Despite the exciting prospects, the existing GCN-based models mainly focus on user–item interactions and seldom consider effectiveness of the side item co-occurrence information on user behavior guidance, resulting in limited performance improvement. Therefore, we propose a novel side item co-occurrence information-aware GCN model. Specifically, we first decouple the original heterogeneous relation graph into corresponding user–item and item–item subgraphs for user–item interaction and item co-occurrence relation modeling. Thereafter, we conduct adaptive iterative aggregation on these subgraphs for user intention understanding and co-occurring item correlation perception. Finally, we present two semantic fusion strategies for sufficient user–item semantic collaborative learning, thereby boosting the overall recommendation performance. Extensive comparison experiments are conducted on three benchmark datasets to justify the superiority of our model.

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Data availability

The datasets generated and analyzed during the current study are available at https://github.com/garfieldcat1985/cogcn.

Notes

  1. https://github.com/garfieldcat1985/cogcn

  2. A symmetric matrix structure is convenient for the model implementation in programming practice.

  3. http://deepyeti.ucsd.edu/jianmo/amazon/index.html.

  4. https://www.tensorflow.org.

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Acknowledgements

This work was supported in part by the Key R &D Program of Shandong Province, China (Major Scientific and Technological Innovation Projects), No.:2022CXGC020107; in part by the National Natural Science Foundation (NSF) of China, No.:62276155, No.:62206156, No.:72004127, and No.:62206157; in part by the NSF of Shandong Province, No.:ZR2021MF040 and No.:ZR2022QF047; in part by the Alibaba Group through Alibaba Innovative Research Program, No.:21169774.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, Shandong, China

    Xinxiao Zhao, Hao Liu, Mingzhu Xu, Haoyu Tang, Xueqing Li & Yupeng Hu

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Fan Liu

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  1. Xinxiao Zhao
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  2. Fan Liu
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  3. Hao Liu
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Correspondence to Yupeng Hu.

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Zhao, X., Liu, F., Liu, H. et al. CoGCN: co-occurring item-aware GCN for recommendation. Neural Comput & Applic 35, 25107–25120 (2023). https://doi.org/10.1007/s00521-023-08703-w

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