Application of Content-Base Recommendation Algorithms on Mobile Travel Applications

Tourism is a major contributor to economic, social, and cultural development for countries and territories. It allows for the understanding of different cultures and customs, and the digital era, with its abundance of mobile technology, allows travellers to experience touristic activities while staying connected to the digital world. Mobile technologies can enhance the traveller’s experience and provide accurate recommendations. We propose a novel mobile recommendation system, which includes its architecture, components, and relevant features. The results showed that the K-means clustering model used is suitable, as evaluated by the Silhouette score, Calinski-Harabasz, and Davies-Bouldin indexes.