[u' @incollection{castillejo_social_2012, address = {Vitoria-Gasteiz, Spain}, title = {Social network analysis applied to recommendation systems: alleviating the cold-user problem}, shorttitle = {Social network analysis applied to recommendation systems}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35377-2_42}, abstract = {Recommender systems have increased their impact in the Internet due to the unmanageable amount of items that users can find in the Web. This way, many algorithms have emerged filtering those items which best fit into users\u2019 tastes. Nevertheless, these systems suffer from the same shortcoming: the lack of new user data to recommend any item based on their tastes. Social relationships gathered from social networks and intelligent environments become a challenging opportunity to retrieve data from users based on their relationships, and social network analysis provides the demanded techniques to accomplish this objective. In this paper we present a methodology which uses users\u2019 social network data to generate first recommendations, alleviating the cold-user limitation. Besides, we demonstrate that it is possible to reduce the cold-user problem applying our solution to a recommendation system environment.}, urldate = {2013-09-30}, booktitle = {Ubiquitous {Computing} and {Ambient} {Intelligence}}, publisher = {Springer}, author = {Castillejo, Eduardo and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, year = {2012}, keywords = {Artificial Intelligence, Data analysis, cold-user problem, collaborative filtering, machine learning, recommendation systems, social network analysis, social networks, thofu}, pages = {306--313}, } ']