@inproceedings{847ecc47f31047ac9df3b27bd08e40b2,
title = "Scenery-Based Fashion Recommendation with Cross-Domain Geneartive Adverserial Networks",
abstract = "To build an effective fashion recommendation system is a still challenging issue due to its high complexity. Previous research works generally have focused on how to provide fashion items visually similar to the user's current fashion taste. However, a scenery (natural landscape) around users is also an important affective factor in recommending fashions. This paper presents a novel system to recommend fashion designs that fit target sceneries. To address this, the exemplar photos regarding the target landscape are first collected from the database. Afterwards, a cross-domain generative adversarial network (GAN) is applied to generate fashion designs from the sceneries. The experimental results demonstrate the feasibility of the proposed system and imply further research directions.",
keywords = "deep learning, fashion recommendation, generative adversarial network, travel assistant",
author = "Jo, \{Sang Young\} and Jang, \{Sun Hye\} and Cho, \{Hee Eun\} and Jeong, \{Jin Woo\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 ; Conference date: 27-02-2019 Through 02-03-2019",
year = "2019",
month = apr,
day = "1",
doi = "10.1109/BIGCOMP.2019.8679117",
language = "English",
series = "2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings",
}