Toward Preference and Context-Aware Hybrid Tourist Recommender System Based on Machine Learning Techniques

Abstract

With the development of machine learning, to improve the accuracy in recommendation systems, the main purpose of the suggested approach consists of using techniques and algorithms which can predict and suggest relevant tourist services (k-items) to users according to their interests, needs, or tastes. In this research, we describe how machine learning techniques can automatically provide personalized recommendations to the requestors by considering both their preferences and their implicit/explicit contextual information and the current contextual constraints of the points of interest. We build an efficient, intelligent H-RN algorithm that hybridizes both the most known machine learning algorithms, namely Random Forest and Naïve Bayes, with both collaborative filtering techniques (model-based and memory-based technique). Different experiments of our approach as part of a recommender system in the touristic field are performed over the four large real-world datasets. Recommender systems can use H-RN to improve recommendation prediction and reduce the search space of tourist services. Moreover, the results of recall, precision, accuracy, F-measure, and average rate, as well as a set of statistical tests (One-way ANOVA, Diversity) and error metrics (RMSE, MAE) have been discussed to show the improvement of the prediction accuracy of our algorithm compared to the baseline approaches in various settings.