Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
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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.