The comparison of the proposed recommended system with actual data

Abstract

Recommendation systems for the museum have been active in the past decade. It used to be a difficult task to make the personalized recommended list for museum-goer. However, with the current technology, research can provide the list for visitors via technology such as mobile applications. We have proposed a recommendation system based on social filtering and statistical methods in the previous paper. This paper applies the F1-score to evaluate our recommendation methods on the actual visitor loggers from Chao sampradaya national museum. We compare the social filtering method with the statistical method and benchmark with the random recommendation. In comparison, the statistical method gives the same result as social filtering when the time is limited. The longer time the visitor spends in the museum, the better result from the social filtering. However, in terms of calculation complexity, the statistical method outperforms social filtering.