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http://dx.doi.org/10.6109/jicce.2022.20.2.131

Evaluations of Museum Recommender System Based on Different Visitor Trip Times  

Sanpechuda, Taweesak (Department of LAI, National Electronics and Computer Technology Center)
Kovavisaruch, La-or (Department of LAI, National Electronics and Computer Technology Center)
Publication Information
Journal of information and communication convergence engineering / v.20, no.2, 2022 , pp. 131-136 More about this Journal
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
Evaluation; Museum; Recommendation; Similarity; Statistic;
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Times Cited By KSCI : 1  (Citation Analysis)
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