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A Review of Explainable Fashion Compatibility Modeling Methods

Published: 28 June 2024 Publication History

Abstract

The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general characteristics of publicly available datasets that are related to the fashion compatibility recommendation task. Finally, we analyzed the representation bias of datasets, fashion-based algorithms’ sustainability, and explainable model assessment. The paper describes practical problem explanations, methodologies, and published datasets that may serve as an inspiration for further research. The proposed structure of the survey organizes knowledge in the fashion recommendation domain and will be beneficial for those who want to learn the topic from scratch, expand their knowledge, or find a new field for research. Furthermore, the information included in this paper could contribute to developing an effective and ethical fashion-based recommendation system.

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Published In

cover image ACM Computing Surveys
ACM Computing Surveys  Volume 56, Issue 11
November 2024
977 pages
EISSN:1557-7341
DOI:10.1145/3613686
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2024
Online AM: 11 May 2024
Accepted: 23 April 2024
Revised: 28 October 2023
Received: 12 October 2022
Published in CSUR Volume 56, Issue 11

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  1. Fashion recommendation
  2. deep learning approaches
  3. model explainability

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