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http://dx.doi.org/10.14400/JDC.2019.17.10.265

Implementation of Machine Learning-Based Art Work Recommendation Service in Embedded System Environments  

Cheon, Mi-Hyeon (Dept. Computer and Communication Engineering, Daegu University)
Lee, Donghwa (Dept. Computer and Communication Engineering, Daegu University)
Publication Information
Journal of Digital Convergence / v.17, no.10, 2019 , pp. 265-271 More about this Journal
Abstract
The number of galleries across the country is increasing as interest in cultural life increases due to the increase in national income. However, museum satisfaction is relatively low compared to other services. In this paper, we propose a service that provides preference information based on machine learning in embedded system environment in order to increase museum satisfaction. The proposed algorithm implements an embedded system using Raspberry Pi. Machine learning was used to find works similar to the viewer's favorite works, and several models were compared to select models applicable to embedded systems. By using the preference information, it is possible to effectively organize the gallery exhibition contents to increase the exhibition satisfaction and the re-visit rate of the museum.
Keywords
Embedded System; Image Processing; Machine Learning; Art Work Recommendation; Art Gallery Service;
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Times Cited By KSCI : 4  (Citation Analysis)
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