ADGRL:A Deep Graph Reinforcement Learning Model with Attention for Online Judge Exercise Recommendation

Online Judge (OJ) systems provide a valuable platform for independent programming skill improvement. However, the sheer number of exercises available often overwhelms users when selecting practice problems. To address this issue, we present the Attention-based Deep Graph Reinforcement Learning algorithm (ADGRL) for exercise recommendation. ADGRL employs graph convolutional neural networks to effectively model user-problem relationships and incorporates an attention mechanism for enhanced personalized recommendations. The model is trained using reinforcement learning to better understand user behavior and provide precise suggestions. Our model is evaluated and trained on a dataset with 14,818 users, 18,171 problems, and 2,887,011 interactions. Comparative experiments with recent graph-based recommendation models demonstrate that ADGRL outperforms them in terms of recall and normalized discounted cumulative gain.