International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Adaptive Recommendation System for Tourism by Personality Type Using Deep Learning

  • Jeong, Chi-Seo (Department of Information System KwangWoon University Graduate School of Smart Convergence) ;
  • Lee, Jong-Yong (Ingenium College of liberal arts, Kwangwoon University) ;
  • Jung, Kye-Dong (Ingenium College of liberal arts, Kwangwoon University)
  • Received : 2019.11.24
  • Accepted : 2019.12.04
  • Published : 2020.02.29
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Abstract

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

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References

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