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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2019,11,30]]},"abstract":"<jats:p>In modern society, people tend to prefer fashionable and decent outfits that can meet more than basic physiological needs. In fact, a proper outfit usually relies on good matching among complementary fashion items (e.g., the top, bottom, and shoes) that compose it, which thus propels us to investigate the automatic complementary clothing matching scheme. However, this is non-trivial due to the following challenges. First, the main challenge lies in how to accurately model the compatibility between complementary fashion items (e.g., the top and bottom) that come from the heterogeneous spaces with multi-modalities (e.g., the visual modality and textual modality). Second, since different features (e.g., the color, style, and pattern) of fashion items may contribute differently to compatibility modeling, how to encode the confidence of different pairwise features presents a tough challenge. Third, how to jointly learn the latent representation of multi-modal data and the compatibility between complementary fashion items contributes to the last challenge. Toward this end, in this work, we present an end-to-end attention-based neural framework for the compatibility modeling, where we introduce a feature-level attention model to adaptively learn the confidence for different pairwise features. Extensive experiments on a public available real-world dataset show the superiority of our model over state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3368071","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T13:12:30Z","timestamp":1576501950000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["An End-to-End Attention-Based Neural Model for Complementary Clothing Matching"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1151-6040","authenticated-orcid":false,"given":"Jinhuan","family":"Liu","sequence":"first","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Xuemeng","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Liqiang","family":"Nie","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Tian","family":"Gan","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]}],"member":"320","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. 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Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, and Fangxi Zhang. 2017. A hybrid collaborative filtering model with deep structure for recommender systems. In Proceedings of the AAAI International Conference on Artificial Intelligence. 1309--1315."},{"volume-title":"Proceedings of the ACM International Conference on Multimedia. 1078--1086","author":"Han Xintong","key":"e_1_2_1_6_1","unstructured":"Xintong Han , Zuxuan Wu , Yu-Gang Jiang , and Larry S. Davis . 2017. Learning fashion compatibility with bidirectional LSTMS . In Proceedings of the ACM International Conference on Multimedia. 1078--1086 . Xintong Han, Zuxuan Wu, Yu-Gang Jiang, and Larry S. Davis. 2017. Learning fashion compatibility with bidirectional LSTMS. 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In Proceedings of the ACM International Conference on Multimedia. 129--138."},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","volume":"1","author":"Iwata Tomoharu","year":"2011","unstructured":"Tomoharu Iwata , Shinji Wanatabe , and Hiroshi Sawada . 2011 . Fashion coordinates recommender system using photographs from fashion magazines . In Proceedings of the International Joint Conference on Artificial Intelligence , Vol. 1 . 2. Tomoharu Iwata, Shinji Wanatabe, and Hiroshi Sawada. 2011. Fashion coordinates recommender system using photographs from fashion magazines. In Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 1. 2."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623332"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3152114"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184745"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv:1408.5882.  Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv:1408.5882.","DOI":"10.3115\/v1\/D14-1181"},{"volume-title":"Proceedings of the International Conference on Neural Information Processing Systems. 1097--1105","author":"Krizhevsky Alex","key":"e_1_2_1_17_1","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E. Hinton . 2012. ImageNet classification with deep convolutional neural networks . 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