{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T05:10:53Z","timestamp":1760764253571,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chengdu Academy of Social Sciences","award":["2024CS116"],"award-info":[{"award-number":["2024CS116"]}]},{"name":"Key Laboratory of Digital Protection and Intelligent Sharing of Traditional Local Opera Resources (Chengdu University of Technology), Sichuan Provincial Department of Culture and Tourism","award":["24XQZD02","24XQYB05"],"award-info":[{"award-number":["24XQZD02","24XQYB05"]}]},{"DOI":"10.13039\/501100013281","name":"Sichuan Mineral Resources Research Center","doi-asserted-by":"crossref","award":["SCKCZY2025-YB004"],"award-info":[{"award-number":["SCKCZY2025-YB004"]}],"id":[{"id":"10.13039\/501100013281","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/info16100902","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:07:22Z","timestamp":1760706442000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GCHS: A Custodian-Aware Graph-Based Deep Learning Model for Intangible Cultural Heritage Recommendation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3449-1218","authenticated-orcid":false,"given":"Wei","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Management Science, ChengDu University of Technology, No. 1 East 3 Road, ErXian Bridge, ChengHua District, Chengdu 610059, China"}]},{"given":"Bowen","family":"Yu","sequence":"additional","affiliation":[{"name":"Chengdu Zero-One Era Technology Co., Ltd., No. 500 Middle Section of Tianfu Avenue, Dongfang Hope Tianxiang Plaza, Chengdu 610016, China"}]},{"given":"Hanyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Management Science, ChengDu University of Technology, No. 1 East 3 Road, ErXian Bridge, ChengHua District, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, L., Li, J.N., Wang, Z.Y., Liu, W.S., Zhang, S., and Wu, J.T. 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