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EnglishIn recent years, we have witnessed the raising popularity of programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC). In order to train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In this paper we present a dynamic Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN).
A dynamic recommender system for online judges based on autoencoder neural networks / Fantozzi, P.; Laura, L.. - 1236:(2020), pp. 197-205. ((Intervento presentato al convegno 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2020 tenutosi a L´Aquila, Italia [10.1007/978-3-030-52287-2_20].
| Titolo: | A dynamic recommender system for online judges based on autoencoder neural networks | |
| Autori: | Fantozzi, Paolo (Primo) LAURA, Luigi (Ultimo) | |
| Data di pubblicazione: | 2020 | |
| Serie: | ||
| Citazione: | A dynamic recommender system for online judges based on autoencoder neural networks / Fantozzi, P.; Laura, L.. - 1236:(2020), pp. 197-205. ((Intervento presentato al convegno 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2020 tenutosi a L´Aquila, Italia [10.1007/978-3-030-52287-2_20]. | |
| Handle: | http://hdl.handle.net/11573/1449336 | |
| ISBN: | 978-3-030-52286-5 978-3-030-52287-2 | |
| Appartiene alla tipologia: | 04b Atto di convegno in volume |
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| Fantozzi_A-dynamic-recommender_2020.pdf | https://link.springer.com/chapter/10.1007/978-3-030-52287-2_20 | Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione) | Tutti i diritti riservati (All rights reserved) | Administrator Richiedi una copia |
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