Combining Content-Based Filtering Methods for Building a Powerful Hybrid Recommender System to Improve Tourism in Drâa-Tafilalet Area

Abstract

This paper proposes a hybrid content-based recommender system aimed at enhancing personalized tourism experiences and supporting the promotion of tourism in Morocco, with particular emphasis on the Drâa-Tafilalet region. The proposed approach integrates three machine learning models—Decision Tree, k-nearest neighbors, and Support Vector Machine—to predict user ratings for historical tourism sites. Tourist attraction metadata and user-generated reviews are represented using TF-IDF vectorization, while the predictions produced by the individual models are combined through an inverse-error weighting strategy. The system is evaluated using two datasets: a subset of the Yelp dataset comprising reviews of historical buildings in the United States, and a regional Drâa-Tafilalet dataset. Experimental results, assessed using MAE and RMSE, indicate that the weighted hybrid model outperforms the individual recommendation models by achieving lower prediction errors. These findings demonstrate the potential of hybrid content-based recommendation approaches to improve the accuracy of personalized tourism recommendations, support tourist decision-making, and promote underrepresented regional destinations.