Recommending Tasks in Online Judges using Autoencoder Neural Networks

Abstract

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. 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 order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.

On Using Real-Time and Post-Contest Data to Improve the Contest Organization, Technical/Scientific Procedures and Build an Efficient Contestant Preparation Strategy

“…Despite its immense value on organizational planning, the mentioned reports do not cover the information that describes the contestant behavior, which might be interesting to the contestants and their team leaders for retrospective analysis and also provide Technical and Scientific teams with valuable information right on time. In recent years, there has been valuable research and applications of analytical methods and Machine Learning techniques to predict the contestant performance (Alnahhas and Mourtada, 2020) and analysis of the task difficulty (Fantozzi and Laura, 2020;Vegt and Schrijvers, 2019;Pankov and Kenzhaliyev, 2020). Some specialists have provided valuable pedagogical analysis of the key factors that may lead to contestant's success (Tsvetkova and Kiryukhin, 2020;Lodi, 2020).…”