Abstract
Nowadays, online judges are very important to improve programming skills for education and technology companies. For this reason, there are many online judges that include large sets of programming challenges. This creates an information overload problem that affects students due to their lack of expertise in choosing the correct challenge to solve, resulting in frustration and a loss of interest in this topic. To solve this scenario, recommender systems appear, but programming judges have not delved much into it. Consequently, this research aims to evaluate the performance of six selected collaborative filtering techniques via a cloud-based software architecture. To validate our experiments we used real online programming judges like CodeChef and NinjaCoding using cloud based architecture with Amazon Web Services, evaluated through Friedman and Wilcoxon statistical tests. The results indicated that Singular Value Decomposition is the best model evaluated with RMSE metric and the fastest in execution time with big datasets.
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