Similarity Metrics from Social Network Analysis for Content Recommender Systems
| Autor: | Pedro Pablo Gómez Martín, Guillermo Jiménez-Díaz, Marco Antonio Gómez Martín, Antonio A. Sánchez-Ruiz |
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| Rok vydání: | 2016 |
| Předmět: |
Social network
Computer science business.industry Problem statement 020207 software engineering 02 engineering and technology Recommender system 01 natural sciences Test (assessment) World Wide Web Online judge 0103 physical sciences Similarity (psychology) 0202 electrical engineering electronic engineering information engineering 010306 general physics business Social network analysis |
| Zdroj: | Case-Based Reasoning Research and Development ISBN: 9783319470955 ICCBR |
| Popis: | Online judges are online systems that test programs in programming contests and practice sessions. They tend to become big problem live archives, 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 of 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. |
| Databáze: | OpenAIRE |
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