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Hi-GNN: hierarchical interactive graph neural networks for auxiliary information-enhanced recommendation

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Abstract

Networked auxiliary information (e.g., user social network, item transition network, etc.) plays a significant role to alleviate the sparse behavioral information (e.g., click, purchase, rating, etc.) in recent recommender systems, which promotes auxiliary information-enhanced recommendation (AIER) to be flourishing. However, existing studies on AIER-treated auxiliary information and behavioral information independently and ignored complex relationships between two types of information, which leads to suboptimal recommendation performance. Toward to this end, we propose hierarchical interactive graph neural networks, short for Hi-GNN, for AIER. Specifically, we firstly learn the behavioral information and the auxiliary information from user and item sides by recursively performing graph neural networks. And then, we design the hierarchical interaction layer to model the relative importance and the mutual association between the behavioral information and the auxiliary information, which furthermore improves performance of AIER by more rationally integrating networked auxiliary information. Experimental results on three real-world datasets demonstrate that Hi-GNN outperforms state-of-the-art methods on recommendation performance and has better resistance to sparse data.

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Data Availability

All data included in this study are available upon request by contact with the corresponding author.

Notes

  1. https://guoguibing.github.io/librec/datasets.html.

  2. https://snap.stanford.edu/data/loc-gowalla.html.

  3. https://www.kaggle.com/pavansanagapati/ad-displayclick-data-on-taobaocom.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Nos. 72271024, 71871019, 71471016).

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Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Haidian, Beijing, 100083, China

    Xiongtao Zhang & Mingxin Gan

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  1. Xiongtao Zhang
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  2. Mingxin Gan
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Contributions

XZ Conceptualization, Methodology, Data Curation, Software, Validation, Writing - Original Draft, Writing - review and editing. MG Conceptualization, Writing - review and editing, Supervision, Funding acquisition.

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Correspondence to Mingxin Gan.

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Zhang, X., Gan, M. Hi-GNN: hierarchical interactive graph neural networks for auxiliary information-enhanced recommendation. Knowl Inf Syst 66, 115–145 (2024). https://doi.org/10.1007/s10115-023-01949-9

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