@article{
 title = {Similarity metrics from social network analysis for content recommender systems},
 type = {article},
 year = {2017},
 keywords = {Online judges,link prediction,recommender systems,similarity metrics,social network analysis},
 volume = {30},
 id = {9f220bfd-e945-3d76-8bf3-7b3eb674f49a},
 created = {2018-02-26T11:10:15.940Z},
 file_attached = {false},
 profile_id = {9b41408a-6b73-3771-9777-d165eecfa568},
 group_id = {417c88cb-beff-360c-a95a-7fe124855ec9},
 last_modified = {2018-02-26T11:10:15.940Z},
 read = {false},
 starred = {true},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2017 - IOS Press and the authors. All rights reserved. Online judges are online systems that test solutions in programming contests and practice sessions. They tend to become large live repositories of problems, with hundreds, or even thousands, of problems. This wide problem statement availability becomes a challenge for new users who want to choose the next problem to solve depending on their knowledge. This is due to the fact that online judges usually lack meta information about the problems and the users do not express their own preferences either. Nevertheless, online judges collect a rich information about which problems have been attempted, and solved, by which users. In this paper, we consider all this information as a social network, and use social network analysis techniques for creating similarity metrics between problems that can be then used for recommendation.},
 bibtype = {article},
 author = {Jimenez-Diaz, G. and Gómez-Martín, P.P. and Gómez-Martín, M.A. and Sánchez-Ruiz, A.A.},
 doi = {10.3233/AIC-170732},
 journal = {AI Communications},
 number = {3-4}
}

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