Abstract
This research presents a new framework for collocating fashion items from heterogeneous network using structural and textual features. Specifically, we construct a fashion heterogenous network, and extract structural features of fashion items from the heterogenous network by utilizing a GATNE model. Then we propose a fashion collocation model based on the fusion of structural and textual features. Given item pairs, their textual features achieved by a Siamese network and structural features achieved by the GATNE model in advance are fused to generate new features. Our framework was examined on a large-scaled clothing item set. The experiment results demonstrate that our proposed framework is effective in the task of fashion collocation.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant no. 61972112 and no. 61832004, the Guangdong Basic and Applied Basic Research Foundation under Grant no. 2021B1515020088, and the HITSZ-J&A Joint Laboratory of Digital Design and Intelligent Fabrication under Grant no. HITSZ-J&A-2021A01.
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Yu, Q., Xu, W., Wu, Y., Zhang, H. (2021). Learning to Collocate Fashion Items from Heterogeneous Network Using Structural and Textual Features. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_13
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DOI: https://doi.org/10.1007/978-981-16-5188-5_13
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