Matching Problem Statements to Editorials in Competitive Programming

Competitive programming presents challenges for students seeking to enhance programming and algorithmic skills. This research introduces a system that efficiently matches problem statements to editorials that describe the solution, helping students find relevant learning resources. The main component of this system is our learning-to-rank model, which achieves a P@1 score of 0.93, indicating its proficiency in identifying the most relevant editorial for a specific problem statement. While our model is smaller in scale compared to general models like GPT-4, it distinguishes itself with comparable results and notable computational efficiency. Additionally, we have developed a new dataset of 1550 competitive programming problem statements and their editorials. Integrated into a competitive programming platform, it has the potential to evolve into an adaptive learning system, customizing paths based on individual user performance. Our code and data are public at https://github.com/DinuGeorge0019/MatchingProblemStatementsToEditorialsInCP.