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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2016 Vol.2(3): 128-131 ISSN: 2382-6185
doi: 10.18178/ijke.2016.2.3.066

Toward Fashion-Brand Recommendation Systems Using Deep-Learning: Preliminary Analysis

Yuka Wakita, Kenta Oku, and Kyoji Kawagoe
Abstract—Recently, the number of Electronic Commerce users has been rapidly increasing with the spread of the Internet. However, users cannot easily find their preferred clothes items among the enormous number on the Internet. As a method for solving this problem, we propose a fashion-brand recommendation system using a deep learning method. This system increases the likelihood that a user will find his/her favorite clothes items. The user must first determine his/her favorite fashion-brands. In this paper, we evaluate the effectiveness of using a deep learning method in a fashion-brand recommendation system. The preliminary analysis shows that the fashion-brand recommendation method using deep learning can dramatically improve the recommendation accuracy as compared with other machine learning methods.

Index Terms—Deep learning, fashion, recommend.

The authors are with Ritsumeikan University, Kusatsu-shi, Shiga, 525–0058, Japan (e-mail: is0148xe@ed.ritsumei.ac.jp, oku@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp).


Cite: Yuka Wakita, Kenta Oku, and Kyoji Kawagoe, "Toward Fashion-Brand Recommendation Systems Using Deep-Learning: Preliminary Analysis," International Journal of Knowledge Engineering vol. 2, no. 3, pp. 128-131, 2016.

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E-mail: ijke@ejournal.net