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Mohamed Frikha
University of Sfax - MIRACL Laboratory
Mohamed Mhiri
University of Sfax - MIRACL Laboratory
Faiez Gargouri
University of Sfax - MIRACL Laboratory
Vol. 4 No. 1 (2015), Articles, pages 90-106
Accepted: Oct 6, 2015


Tunisia is well placed in terms of medical tourism and has highly qualified and specialized medical and surgical teams. Integrating social networks in Tunisian medical tourism recommender systems can result in much more accurate recommendations. That is to say, information, interests, and recommendations retrieved from social networks can improve the prediction accuracy. This paper aims to improve traditional recommender systems by incorporating information in social network; including user preferences and influences from social friends. Accordingly, a user interest ontology is developed to make personalized recommendations out of such information. In this paper, we present a semantic social recommender system employing a user interest ontology and a Tunisian Medical Tourism ontology. Our system can improve the quality of recommendation for Tunisian tourism domain. Finally, our social recommendation algorithm is implemented in order to be used in a Tunisia tourism Website to assist users interested in visiting Tunisia for medical purposes.


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