Vision‐based adaptive cross‐domain online product recommendation for 3D design models
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Le résumé fourni par la source
Three-dimensional (3D) digital design is extensively adopted in the architecture, engineering, consulting, operations, and maintenance (AECOM) industry to enhance collaboration among stakeholders. Although recommendation systems are commonly employed to facilitate purchasing in e-commerce websites, none involves recommending online products to users from 3D building design models due to dimensional and stylistic discrepancies. This study proposes a vision-based adaptive cross-domain online product recommendation method, VacRed, for 3D building design models. First, a cross-domain approach is proposed to transform design models into e-commerce images, addressing discrepancies in dimension and style between them. Second, an adaptive mechanism is introduced to solve the issue of image quality instability caused by variations in generator weights during the training process of generative models. Third, a cross-domain product recommendation system is developed based on deep learning to recommend the top k relevant online products for a given building design product. Finally, experiments were conducted to ascertain the effectiveness of the VacRed method. The experimental results of this method demonstrate its excellent performance, achieving a precision rate (PR) of 87.20% and a mean average precision of 83.65%. This study effectively connects two main stages in the AECOM industry, design and purchasing, and two large communities, design and e-commerce.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Vision‐based adaptive cross‐domain online product recommendation for 3D design models
- Date Crossref
- 01/08/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture pays non établi dans la noticeUniversité ou école supérieure
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Tsinghua–Berkeley Shenzhen Institute pays non établi dans la noticeStructure de recherche
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Tsinghua University Institute for Ocean Engineering pays non établi dans la noticeUniversité ou école supérieure
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China Energy Engineering Corporation (China) pays non établi dans la noticeEntreprise
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State Development & Investment Corporation (China) pays non établi dans la noticeEntreprise
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China Construction Fifth Engineering Division Corp. Ltd. pays non établi dans la noticeEntreprise
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Beijing Gas Energy Development Corp. Ltd. pays non établi dans la noticeEntreprise
Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture — Beijing University of Civil Engineering and Architecture, Tsinghua–Berkeley Shenzhen Institute et Institute for Ocean Engineering — Tsinghua University, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.