Case-based recommendation for online judges using learning itineraries. Sánchez-Ruiz, A., Jimenez-Diaz, G., Gómez-Martín, P., & Gómez-Martín, M. Volume 10339 LNAI , 2017. doi abstract bibtex © Springer International Publishing AG 2017. Online judges are online repositories with hundreds or thousands of programming exercises or problems. They are very interesting tools for learning programming concepts, but novice users tend to feel overwhelmed by the large number of problems available. Traditional recommendation techniques based on content or collaborative filtering do not work well in these systems due to the lack of user ratings or semantic descriptions of the problems. In this work, we propose a recommendation approach based on learning itineraries, i.e., the sequences of problems that the users tried to solve. Our experiments reveal that interesting learning paths can emerge from previous user experiences and we can use those learning paths to recommend interesting problems to new users. We also show that the recommendation can be improved if we consider not only the problems but also the order in which they were solved.
@book{
title = {Case-based recommendation for online judges using learning itineraries},
type = {book},
year = {2017},
source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10339 LNAI},
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abstract = {© Springer International Publishing AG 2017. Online judges are online repositories with hundreds or thousands of programming exercises or problems. They are very interesting tools for learning programming concepts, but novice users tend to feel overwhelmed by the large number of problems available. Traditional recommendation techniques based on content or collaborative filtering do not work well in these systems due to the lack of user ratings or semantic descriptions of the problems. In this work, we propose a recommendation approach based on learning itineraries, i.e., the sequences of problems that the users tried to solve. Our experiments reveal that interesting learning paths can emerge from previous user experiences and we can use those learning paths to recommend interesting problems to new users. We also show that the recommendation can be improved if we consider not only the problems but also the order in which they were solved.},
bibtype = {book},
author = {Sánchez-Ruiz, A.A. and Jimenez-Diaz, G. and Gómez-Martín, P.P. and Gómez-Martín, M.A.},
doi = {10.1007/978-3-319-61030-6_22}
}
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