A novel ontology framework supporting model-based tourism recommender
Hồ Quốc Dũng, Lien Thi Quynh Le, Tho Huu-Hoang Nguyen, Tri Quoc Truong, Đình Hòa Nguyễn · IAES International Journal of Artificial Intelligence · 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.