Journal of Platform Technology

  • 제11권6호
  • /
  • Pages.21-27
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  • 2023
  • /
  • 2289-0181(pISSN)
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  • 2289-019X(eISSN)
ICT플랫폼학회 (ICT Platform Society)
권호 표지

Deep Reinforcement Learning based Tourism Experience Path Finding

  • Kyung-Hee Park (Dept. of Computer Engineering, Dongguk Univ. ) ;
  • Juntae Kim (Univ. of Dongguk, Dept. of Computer Engineering)
  • 투고 : 2023.10.26
  • 심사 : 2023.12.21
  • 발행 : 2023.12.30
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초록

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.

키워드

과제정보

This research was funded by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport, grant number 1615012545

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