A novel ontology framework supporting model-based tourism recommender - Ho Quoc Dung; Lien Thi Quynh Le; Nguyen Huu Hoang Tho; Tri Quoc Truong; Cuong H. Nguyen-Dinh (Institute of Advanced Engineering and Science, 2021)
In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework.