Online Recommender system for Accessible Tourism Destinations

Traveling for leisure has become an important part of our society. It has proven time and again its benefits for wellbeing and personal growth. There are many types of tourism and one of them is Accessible Tourism (AT), an ongoing endeavor to ensure that everyone, regardless of condition, has the right to benefit from tourism experiences. Recommender systems (RSs) represent a mature technique for generating clear and personalized suggestions. While being widely researched and used by the tourism academic community and the tourism industry in general, Recommender Systems (RSs) can still do much more for Accessible Tourism (AT). This thesis aims to build a recommender system dedicated to recommending accessible tourism destinations and easy the process of e2e trip planning for people with disabilities. With a modular design, use of ontologies, machine learning techniques and a "start small, define expansion, expand" approach, this recommender system, once built, aims to be validated by real users.