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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

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기사명 저자명 페이지 원문 목차
인공지능에 의한 개인 맞춤 패션 스타일 추천 서비스 사례 연구 = A case study on the recommendation services for customized fashion styles based on artificial intelligence 안효선, 권수희, 박민정 p.349-360

소방공무원의 장갑제작을 위한 손 치수 연구 = Hand sizing study for development of firefighting gloves 권채령, 장미나, 정다운, 김동은 p.416-426

한국 여자 군인 현 방한복의 치수 및 동작적합성 만족도에 관한 연구 = Satisfaction on fitness and motion suitability of Korean female military winter jacket 한현숙, 한현정 p.361-372
패션콘텐츠 미디어 환경 예측을 위한 해외 SPA 브랜드의 SNS 언어 네트워크 분석 = Estimating media environments of fashion contents through semantic network analysis from social network service of global SPA brands 전여선 p.427-439

직물을 구성하는 실의 시각적 혼색 효과 = Visual color mixing effect of yarns in textile fabrics 채영주 p.373-383
소비자 관점의 의복 디자인 평가 요소에 대한 탐색적 연구 = An exploratory study on apparel design evaluation criteria with consumers' perspectives : focusing on female college students majoring in apparel-fashion design in their 20s : 20대 의류-패션 디자인 전공 여대생을 중심으로 김선우 p.384-404

3D 인체데이터를 활용한 남성 정장재킷 패턴개발 연구 = A study on development of men's formal jacket pattern by 3D human body scan data : a focus on men's in their late 30s : 30대 후반 남성을 중심으로 신경희, 서추연 p.440-458
패션테라피 효과 유지에 대한 질적 접근 = Qualitative approach to maintaining the effect of fashion therapy 이새은, 이유리 p.311-326

고령 남자의 겨울철 모자 착용 효과 = Physiological and psychological effects of wearing winter cap in elderly males : 생리·심리적 검토 박준희, 이주영 p.405-415

Environmentally responsible apparel consumption and convertible dresses Sumin Koo, Yoon Jin Ma p.327-348

참고문헌 (40건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
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15 Kim, J. M. (2018, December 9). 집짓는 3D프린터⋅제트엔진비행슈트...美타임 ‘올해의 발명품’ 눈길 [Home-building 3D printer⋅jet-engine flight suit...American Time's ‘Inventions of the Year,’ is drawing attention]. Newsis. Retrieved from http://www.newsis.com/view/?id=NISX20181207_0000495667&cID=13001&pID=13000 미소장
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17 Experimental evidence of massive-scale emotional contagion through social networks. 네이버 미소장
18 Lee, J. Y. (2019, January 28). 입고 싶은 옷 사진 올리니...AI 가 찾아주네 [Upload a picture of what you want to wear, and AI will find it]. Sedaily. Retrieved from https://www.sedaily.com/NewsView/1VE7P9OIYN 미소장
19 Lee, K. H. (2018, April 15). 오드컨셉 시각지능AI ‘딥룩’, 패션상품 검색⋅추천 IMB ‘왓슨’과 협력 [Odd concept visual intelligence AI ‘Deep look’, search for fashion products and work with recommended IMB ‘Watson’]. The DigitalTimes. Retrieved from http://www.dt.co.kr/contents.html?article_no=2018041702101552660001&ref=naver 미소장
20 Lee, S. (2016). The effects of fashion curation service quality attributes on satisfaction, trust and purchase intention (Unpublished master's thesis). Yonsei University, Seoul. 미소장
21 Matsuo, Y. (2015). 인공지능과 딥러닝 [AI and deep learning](K. W. Park, Trans.). Seoul: Dongamnb. (Original work published 2015) 미소장
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24 Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. London: Penguin Books. 미소장
25 Park, C. (2018, August). 엄마가 내 몸에 꼭 맞는 옷 만들어준 듯 초연결 시대엔 ‘감동의 맞춤 패션’ 뜬다 [Just as the mother makes clothes that fit my body, the ‘impressive customized fashion’ of the hyper-connected age is drawing attention]. Dong-A Business Review. Retrieved from http://dbr.donga.com/article/view/1202/article_no/8774 미소장
26 Park, Y. N., & Kim, C. S. (2017). A study on the influence of fashion interest and personal taste on their attitude of Fashion Curation Service (FCS) and purchase intention of fashion products-Focused on people aged 20s to 30s who experienced FCS-. Journal of Basic Design & Art, 18(1), 173‒188. 미소장
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36 Wei, S., Ye, N., Zhang, S., Huang, X., & Zhu, J. (2012, August). Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure. Proceedings of the 2012 International Conference on Computer Science and Service System, China, 2038‒2041. doi:10.1109/CSSS.2012.507 미소장
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