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DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graph

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Abstract

User–item bipartite graphs and knowledge graphs are frequently employed in recommender systems due to their ability to provide rich information for user and item modeling. However, existing recommender systems predominantly focus on modeling either the user or item individually, with few studies simultaneously considering both aspects. In this paper, we propose a novel double-ended recommendation model, DEKGCI, which aims to fully leverage the advantages of these two information sources. Specifically, DEKGCI harnesses the high-order connectivity information in the user–item bipartite graph to extract user representations, while utilizing the semantic information in the knowledge graph to enrich item representations. By doing so, DEKGCI concurrently learns both user and item representations, effectively capturing the intricate interaction information between users and items. The DEKGCI model was evaluated on three real-world datasets. Computational results demonstrate the high effectiveness of the proposed DEKGCI model compared to seven state-of-the-art reference methods from the literature. In particular, compared to the best-performing KFGAN model, DEKGCI achieved AUC gains of 0.335% in movie recommendations, and F1 gains of 0.023%, 2.203%, and 0.530% in movie, book, and music recommendations, respectively. The code and data are available at https://github.com/miaomiao924/DEKGCI.

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Data availability

The codes and the data are available at https://github.com/miaomiao924/DEKGCI.

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Acknowledgements

This work was financially supported by the Research Funds from the National Natural Science Foundation of China (Grant No. 62077019).

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Authors and Affiliations

  1. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China

    Yajing Yang, Zeyu Zeng, Shiyun Jiang, Mao Chen & Ruirui Shang

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  1. Yajing Yang
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  2. Zeyu Zeng
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  3. Shiyun Jiang
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  4. Mao Chen
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  5. Ruirui Shang
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Contributions

Y.Y. contributed to the conceptualization and methodology of the manuscript. Z.Z. drafted the main manuscript. S.J. refined the manuscript. M.C. reviewed and edited the manuscript. R.S. conducted the experiments and prepared the figures and tables. All authors reviewed the manuscript."

Corresponding author

Correspondence to Mao Chen.

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Yang, Y., Zeng, Z., Jiang, S. et al. DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graph. J Supercomput 80, 24781–24800 (2024). https://doi.org/10.1007/s11227-024-06344-x

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