Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Evaluations of Museum Recommender System Based on Different Visitor Trip Times

  • Received : 2021.10.27
  • Accepted : 2021.12.10
  • Published : 2022.06.30
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Abstract

The recommendation system applied in museums has been widely adopted owing to its advanced technology. However, it is unclear which recommendation is suitable for indoor museum guidance. This study evaluated a recommender system based on social-filtering and statistical methods applied to actual museum databases. We evaluated both methods using two different datasets. Statistical methods use collective data, whereas social methods use individual data. The results showed that both methods could provide significantly better results than random methods. However, we found that the trip time length and the dataset's sizes affect the performance of both methods. The social-filtering method provides better performance for long trip periods and includes more complex calculations, whereas the statistical method provides better performance for short trip periods. The critical points are defined to indicate the trip time for which the performances of both methods are equal.

Keywords

Acknowledgement

We would like to thank Chao Sam Phraya Museum and Schwadogon Museum, essential resources for this study. We also wish to thank Professor Myint Myint Sein and her students from the University of Computer Study Yangon, who supported the findings and facilitated contact with resources in Myanmar.

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