• 한국멀티미디어학회논문지
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• 제25권3호
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• pp.537-545
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• 2022

Emotional experience (James, 1884)[1] can affect people's behavior. There are few types of research on VR marketing(Maojun Zhou, Zeru Yan, 2018)[2] design based on emotional experience. This article is based on emotional evaluation theory and empirical research, and the VR marketing case "Buy+" online shopping platform (Wu Yongyi, 2016). It is concluded that there are three levels of emotional experience definition on VR marketing which decompose the features of the VR marketing design of "Buy+ as an online-shop" correspondingly and find out the design features of VR marketing from the perspective of emotional experience. Finally, through the analysis of the questionnaire data, it verified that vividness, functionality and effectiveness could represent the features of VR marketing design. Moreover, it analyzed the correlation among these factors. Vividness and functionality have the closest relationship among them. The definition, the components, and the correlation of the three-layer emotional experience obtained from this research can provide theoretical support and reference for other VR marketing designs.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.560-561
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• 2018

스마트폰의 보급으로 온라인 쇼핑 시장에서 모바일 쇼핑의 비중이 확대되고 있다. 대부분의 모바일 쇼핑은 애플리케이션을 통해 서비스를 제공하고 있다. 기업들은 온라인 마켓의 경쟁력 확보와 소비자의 다양한 요구사항 응대를 위해 개인화 서비스를 제공한다. 하지만 개인화 서비스는 사용자 데이터 수집과 분석이 매우 중요하다. 따라서 본 논문에서는 모바일 쇼핑 환경의 사용자 데이터 수집을 위해 스마트폰의 카메라를 이용하여 물품의 바코드 인식기능과 머신러닝 기반 물품의 이미지 인식 기능을 구현하였다. 구현된 기능과 푸시 알림 서비스를 통해 온라인 쇼핑 플랫폼 애플리케이션의 개인화 서비스와 사용자 데이터 수집 및 분석을 할 수 있었다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.465-467
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• 2022

Mobile phones have become an essential item nowadays since it provides access to online platform and service fast and easy. Coming to these platforms such as Social Network Service (SNS) for shopping have been a go-to option for many people. However, searching for a specific fashion item in the picture is challenging, where users need to try multiple searches by combining appropriate search keywords. To tackle this problem, we propose a system that could provide immediate access to websites related to fashion items. In the framework, we also propose a deep learning model for an automatic analysis of image contexts using instance segmentation. We use transfer learning by utilizing Deep fashion 2 to maximize our model accuracy. After segmenting all the fashion item objects in the image, the related search information is retrieved when the object is clicked. Furthermore, we successfully deploy our system so that it could be assessable using any web browser. We prove that deep learning could be a promising tool not only for scientific purpose but also applicable to commercial shopping.