DATAtourist

Abstract

Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.