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).
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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.
