• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.527-543
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• 2020

The aim of this study is develop a state-funded education and training curriculum to contribute to the development of the fashion industry infrastructure. This will be achieved based on the service sector by the competitive clothing sales personnel and fashion startups. The study was conducted using a qualitative research method. The participants were 20 representatives of fashion-related companies and employees from one traditional market and two fashion outlets in Seogu, Gwangju. Data was collected from September 2015 to January 2017 by demand surveys and in-depth interviews. These were conducted on the same day at each clothing store office. In addition, existing literature was also reviewed. The collected data were first summarized into 64 meaning units from which three themes were derived by arranging, classifying, and analyzing the data. The findings of the study are as follows. First, the education and training curriculum for fashion job creation is aimed at job-oriented field-types with the objective of cultivating professional skills for online to offline fashion professionals. Second, the curriculum for fashion advisors was developed to consisted of 8 courses of 150 hours, including job knowledge, a foreign language, fashion-specific knowledge, fashion marketing & VMD, store management know-how, clothing repair, field trip, and internship. Third, the curriculum for fashion entrepreneurs consisted of 8 courses of 106 hours, including entrepreneurship, fashion practice, startup, field trip, finance & taxation accounting, marketing, social enterprise course, and internship.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.