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GCHS: A Custodian-Aware Graph-Based Deep Learning Model for Intangible Cultural Heritage Recommendation

Xiao, Wei, Yu, Bowen, Zhang, Hanyue·2025·Information
DOI 10.3390/info16100902
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Abstract

Digital platforms for intangible cultural heritage (ICH) function as vibrant electronic marketplaces, yet they grapple with content overload, high search costs, and under-leveraged social networks of heritage custodians. To address these electronic-commerce challenges, we present GCHS, a custodian-aware, graph-based deep learning model that enhances ICH recommendation by uniting three critical signals: custodians’ social relationships, user interest profiles, and content metadata. Leveraging an attention mechanism, GCHS dynamically prioritizes influential custodians and resharing behaviors to streamline user discovery and engagement. We first characterize ICH-specific propagation patterns, e.g., custodians’ social influence, heterogeneous user interests, and content co-consumption and then encode these factors within a collaborative graph framework. Evaluation on a real-world ICH dataset demonstrates that GCHS delivers improvements in Top-N recommendation accuracy over leading benchmarks and significantly outperforms in terms of next-N sequence prediction. By integrating social, cultural, and transactional dimensions, our approach not only drives more effective digital commerce interactions around heritage content but also supports sustainable cultural dissemination and stakeholder participation. This work advances electronic-commerce research by illustrating how graph-based deep learning can optimize content discovery, personalize user experience, and reinforce community networks in digital heritage ecosystems.

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Metadata

Authors
Xiao, W., Yu, B., & Zhang, H.
Venue
Information
Publisher
MDPI AG
Published
2025-10-15
Volume
16
Issue
10
Pages
902
ISSN
2078-2489
DOI
10.3390/info16100902
Cited by
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Chengdu Academy of Social Sciences (2024CS116)
  • Key Laboratory of Digital Protection and Intelligent Sharing of Traditional Local Opera Resources (Chengdu University of Technology), Sichuan Provincial Department of Culture and Tourism (24XQZD02, 24XQYB05)
  • Sichuan Mineral Resources Research Center (SCKCZY2025-YB004)

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CoChat. (2026). Citation check: GCHS: A Custodian-Aware Graph-Based Deep Learning Model for Intangible Cultural Heritage Recommendation. https://app.cochat.ai/papers/10.3390/info16100902
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Xiao, W., Yu, B., & Zhang, H. (2025). GCHS: A Custodian-Aware Graph-Based Deep Learning Model for Intangible Cultural Heritage Recommendation. Information. https://doi.org/10.3390/info16100902

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