Ontology-based Matchmaking to Provide Personalized Recommendations for Tourists

Abstract

This paper addresses the challenges to support tourists in their decision-making during the pre-trip phase and to facilitate the process of identifying those tourism objects that best fit the tourists´ preferences. The latter directly depends on the quality of the matchmaking process, i.e. finding those tourism objects that are most attractive to a particular tourist. To achieve this goal, an innovative approach is introduced that matches tourist profiles with the characteristics of tourism objects in order to obtain a ranked list of appropriate objects for a particular tourist. The matchmaking process leverages tourist factors as a shortcut to propose a first user profile and related to this, a first set of tourism objects. User feedback is then used to dynamically adapt the tourist profile and thus refine the set of recommended objects. Our approach is tested through a prototypical recommender system that suggests tourists in Vienna attractions that are tailored to their personal needs. Furthermore, a user study is conducted by asking people to interact with the system and fill in a questionnaire afterwards.

This paper addresses the challenges to support tourists in their decision-making during the pre-trip phase and to facilitate the process of identifying those tourism objects that best fit the tourists´ preferences. The latter directly depends on the quality of the matchmaking process, i.e. finding those tourism objects that are most attractive to a particular tourist. To achieve this goal, an innovative approach is introduced that matches tourist profiles with the characteristics of tourism objects in order to obtain a ranked list of appropriate objects for a particular tourist. The matchmaking process leverages tourist factors as a shortcut to propose a first user profile and related to this, a first set of tourism objects. User feedback is then used to dynamically adapt the tourist profile and thus refine the set of recommended objects. Our approach is tested through a prototypical recommender system that suggests tourists in Vienna attractions that are tailored to their personal needs. Furthermore, a user study is conducted by asking people to interact with the system and fill in a questionnaire afterwards.