• Journal of Korea Multimedia Society
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• v.25 no.3
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.560-561
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• 2018

The spread of smartphones is increasing the proportion of mobile shopping in the online shopping market. Most mobile shopping services are delivered through applications. However, personalization services are very important for user data collection and analysis. Therefore, in this paper, we implemented the product barcode recognition function and machine learning-based product image recognition function using smartphones camera to collect user data in mobile shopping environment. The implemented function and push notification services enabled the collection and analysis of user data and personalization services for online shopping platform applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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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.