PROFILE REFINEMENT IN ONTOLOGY-BASED RECOMMANDER SYSTEMS FOR ECONOMICAL E-NEWS
Résumé
This paper is interested in a recommender system of economic news articles. More precisely, it focuses on automatic profile refinement of customers which is an important task over time by taken into account logs of the user concerning especially his/her actions, reading time, and domain specific knowledge. In our approach, ontologies are used to describe and automatically refine these profiles. This work focuses on one particular type of recommender systems which is content-based recommenders. The aim of these recommender systems is to build a user profile and to improve its precision over time. Several improvements that have been made to these recommender systems over the last decade are analyzed. We find that the improvements brought by the use of semantic knowledge are not negligible, therefore semantic web approaches should be more and more used in the future. Nevertheless improvements remain possible in this domain and further research could be interesting.