Deep fashion recommendation system with style feature decomposition

Yong Goo Shin, Yoon Jae Yeo, Min Cheol Sagong, Seo Won Ji, Sung Jea Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


Recently, convolutional neural network (CNN) -based fashion recommendation techniques, which automatically recommend the matching clothes to the consumer, have been widely researched. In general, the feature vector of a fashion item, i.e. clothes vector, obtained by CNN conveys two types of information: style and category, where the style indicates the distinctive characteristic of the clothes and the category represents the common properties of the clothes in the same class. Due to the mixed information of style and category, however, the clothes vector often recommends the unmatching clothes. To solve this problem, we propose a style feature extraction (SFE) layer, which effectively decomposes the clothes vector into style and category. Based on the characteristics that the category information has small variations in the same class while being distinguished from other classes, we extract and remove the category information from the clothes vector to obtain more accurate style information. Experimental results show that the proposed method achieves state-of-the-art results in terms of link prediction, which is a performance measure of a stylish match. In addition, as a simple CNN layer, it is expected that the proposed SFE layer is compatible with all popular CNN architectures.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 9th International Conference on Consumer Electronics, ICCE-Berlin 2019
EditorsGordan Velikic, Christian Gross
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781728127453
Publication statusPublished - 2019 Sept
Event9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019 - Berlin, Germany
Duration: 2019 Sept 82019 Sept 11

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822


Conference9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019

Bibliographical note

Funding Information:
This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIP) (No. 2019-0-00268, Development of SW technology for recognition, judgement and path control algorithm verification simulation and dataset generation)

Publisher Copyright:
© 2019 IEEE.


  • Convolutional neural network
  • Deep learning
  • Recommendation system
  • Visual compatibility

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Media Technology


Dive into the research topics of 'Deep fashion recommendation system with style feature decomposition'. Together they form a unique fingerprint.

Cite this