발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.5] An O2O Based Product Recommendation and Augmented Reality Service Architecture Research |
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| 논문투고자 |
Chul-Jin Kim, Tae-Hwan Jang |
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| 논문내용 |
In an online shopping mall, a recommendation technique is used to increase the purchasing power of a user for a product, and an augmented reality service or a virtual reality service is used to increase shopping satisfaction. This study proposes an architecture for providing an offline experience by providing an online recommendation service in an offline shopping mall and providing an augmented reality service for recommended products. The recommendation service uses deep learning techniques rather than the existing traditional collaborative filtering techniques to increase the accuracy of recommendations. In this study, we propose an architecture with high recommendation accuracy by using deep learning-based i-LSTM algorithm for recommendation service. In order to provide an O2O based recommendation service, signals for offline products are received through beacons and online products are recommended. To improve the satisfaction of shopping for online recommended products, we propose an augmented reality service architecture to provide a sense of reality for products through offline product experiences. The augmented reality service is based on the Vuforia augmented reality development platform in terms of mobile applications and servers. We propose a layer architecture and flow architecture for such recommendation and augmented reality service structures, and define functional flows between components and elements. In the experiment, the suitability of the architecture is verified by implementing the product recommendation and augmented reality service for the proposed O2O product recommendation and augmented reality service architecture. |
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