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’ 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’ 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.
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Castillejo, E., Almeida, A., López-de-Ipiña, D. (2012). Social Network Analysis Applied to Recommendation Systems: Alleviating the Cold-User Problem. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_42
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DOI: https://doi.org/10.1007/978-3-642-35377-2_42
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