CONICET | Buscador de Institutos y Recursos Humanos

Recommender Systems learn users´ preferences and tastes in differentdomains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group prole by analyzing not only users´ preferences, but also the social relationships between members of a group.This aspect is a hot research topic in the recommender systems area. Inaddition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filteringtechniques: collaborative, content-based and demographic filtering. Inthis way, the disadvantages of one technique are overcome by the others.Our approach was materialized in a recommender system named Hermes,which suggests tourist attractions to both individuals and groups of users.We have obtained promising results when comparing our approach withclassic approaches to generate recommendations to individual users andgroups. These results suggest that considering the type of users´ rela-tionship to provide recommendations to groups leads to more accuraterecommendations in the tourism domain. These findings can be helpfulfor recommender systems developers and for researchers in this area.