Ricerc@Sapienza

In 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.

Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. 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. Thus, in order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN).