Personalized recommendation of tourism points of interest using classification-based machine learning

Abstract

This study proposes a two-stage personalized recommendation framework for tourism Points of Interest (POIs) based on classification techniques. Unlike conventional approaches that require detailed user–item rating matrices or fine-grained POI-level data, our model predicts each user’s most suitable POI category by leveraging demographic attributes, temporal features, and aggregated preference scores. Once the POI Category is identified, the system retrieves specific destinations from a structured POI database. Using data collected from 1,000 tourists in Hue City, Vietnam, we implement and compare five classification algorithms (Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes). Experimental results demonstrate that Random Forest achieves the highest predictive accuracy and lowest error metrics (MAE, RMSE, MSE), with strong agreement measured by the Kappa statistic. The originality of this research lies in: (i) reframing POI recommendation as a classification-based task suitable for data-scarce environments; (ii) providing empirical evidence from an emerging tourism destination; and (iii) conducting a statistical comparison of multiple algorithms using non-parametric tests (Friedman and Nemenyi). The findings lay a methodological and practical foundation for developing smart tourism systems that deliver effective personalization even with limited rating data.