Journal of the Korea Fashion and Costume Design Association (한국의상디자인학회지)

Korea Fashion and Costume Design Association (한국의상디자인학회)
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Influence of product category and features on fashion recommendation service algorithm

패션 추천서비스 알고리즘에서 상품유형과 속성 조합의 영향

  • Choi, Ji Yoon (Human-Tech Convergence Program, Dept. of Clothing & Textiles, Hanyang University) ;
  • Lee, Kyu-Hye (Human-Tech Convergence Program, Dept. of Clothing & Textiles, Hanyang University)
  • 최지윤 (한양대학교 의류학과 휴먼테크융합전공) ;
  • 이규혜 (한양대학교 의류학과 휴먼테크융합전공)
  • Received : 2022.03.24
  • Accepted : 2022.05.20
  • Published : 2022.06.30
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Abstract

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.

Keywords

References

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