Toward explainable fashion recommendation

Pongsate Tangseng, Takayuki Okatani

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

Abstract

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item-feature pair correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2142-2151
Number of pages10
ISBN (Electronic)9781728165530
DOIs
Publication statusPublished - 2020 Mar
Event2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States
Duration: 2020 Mar 12020 Mar 5

Publication series

NameProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

Conference

Conference2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
CountryUnited States
CitySnowmass Village
Period20/3/120/3/5

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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