Machine Learning for Exhibition Recommendation in a Museum’s Virtual Tour Application

Abstract

The museum visit is having a crisis during the COVID-19 pandemic. SMBII Museum in Palembang has a remarkable decrease of visitors up to 90%. A strategy is needed to increase museum visits and enable educational and tourism roles in a pandemic situation. This paper evaluates the machine learning model for exhibition recommendations given to visitors through virtual tour applications. Exploring unfamiliar museum exhibitions to visitors through virtual museum applications will be tedious. If virtual collections are ancient and do not display any interest, they will quickly lead to boredom and reluctance to explore virtual museums. For this reason, an effective method is needed to provide suggestions or recommendations that meet the interests of visitors based on the profiles of museum visitors, making it easier for visitors to find exciting exhibition rooms for learning and tourism. Machine learning has proven its effectiveness for predictions and recommendations. This study evaluates several machine learning classifiers for exhibition recommendations and development of virtual tour applications that applied machine learning classifiers with the best performance based on the model evaluation. The experimental results show that the KNN model performs best for exhibition recommendations with cross-validation accuracy = 89.09% and F-Measure = 90.91%. The SUS usability evaluation on the exhibition recommender feature in the virtual tour application of SMBII museum shows average score of 85.83. The machine learning-based recommender feature usability is acceptable, making it easy and attractive for visitors to find an exhibition that might match their interests.