CAFOB: Context-aware fuzzy-ontology-based tourism recommendation system

The ever-increasing volume of information available on tourist attractions in cyberspace has made the tourist decision-making process a crucial task. Therefore, tourism recommendation systems can significantly benefit tourists in terms of comfort and satisfaction. In this paper, a context-aware fuzzy-ontology-based tourism recommendation system is proposed. In the proposed system, we have two new propositions that can be individually used in other tourism recommendation systems: a fuzzy-weighted ontology and a new sentiment/emotion score scheme. In CAFOB, the ontology-based scores of a user’s reviews are then multiplicatively modulated by sentiment/emotion scores to generate the total scores of the reviews’ ontology words, representing the user preferences. Then, the nearby-open-3 +-bubbled touristic attractions’ characteristics are extracted based on their past visitors’ reviews. Finally, a recommendation list is produced using a maximum hybrid semantic similarity between the user preferences and attractions’ characteristics. The employment of contextual information, including weather, location, and time makes CAFOB context-aware and improves the accuracy and quality of the recommendations. The F-measure, NDCG, and MRR results show the outperformance of CAFOB against the state-of-the-art tourism recommendation systems for all relevant Top N recommendation options and different geographical spans of interest. • A fuzzy-weighted ontology that is more representative of real life. • CAFOB benefits from the advantages of ontology, semantics, and context-awareness. • A higher accuracy than cluster-based TRSs by considering each keyword individually. • A new TRS feature-based taxonomy that provides an insightful research framework.