@inproceedings{c4abb28541d847f8b49c4e113a029a5b,
title = "Clothes Collocation Recommendations by Compatibility Learning",
abstract = "This paper introduces a simple, yet effective, framework for clothes collocation by considering compatibility between items. In particular, we treat title sentences as the features of clothing items, instead of using clothing images. For feature transformation, the long-short term memory (LSTM) network is utilized for mapping title sentences into a low-dimensional space. Features of query and candidate items learned by the Siamese LSTMs are synthesized into a style space by a compatibility matrix. We evaluate our framework on two large-scale datasets compiled from Amazon and Taobao, respectively. Extensive experimental results show the effectiveness of our method in comparison to several state-of-the-art methods.",
keywords = "Cloth collocation, Compatibility, Recommendation, Siamese LSTM",
author = "Haijun Zhang and Wang Huang and Linlin Liu and Xiaofei Xu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Web Services, ICWS 2018 ; Conference date: 02-07-2018 Through 07-07-2018",
year = "2018",
month = sep,
day = "5",
doi = "10.1109/ICWS.2018.00030",
language = "英语",
isbn = "9781538672471",
series = "Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "179--186",
booktitle = "Proceedings - 2018 IEEE International Conference on Web Services, ICWS 2018 - Part of the 2018 IEEE World Congress on Services",
address = "美国",
}