基于深度强化学习与程序分析的OJ习题推荐模型

At present, there are a large number of exercises on the existing programming Online Judge systems(OJ), which makes it difficult for studenti to quickly find suitable exercises according to their own knowledge level and learning demand. Therefore, it is necessary to design a model to recommend suitable exercises to students. However, due to uniqueness of OJ and complexity of programming ability evaluation, existing recommendation model can not complete OJ exercise recommendation task well, the main Problems include: OJ exercises' lack of knowledge label and unique proposition style make it difficult for existing models to mine correlation between exercises; actual correctness of the program submitted by Student is inconsistent with OJ judgement resuit, which leads to deviation of students' knowledge state estimated by models; existing models are difficult to provide exercises that increase students' programming ability most significantly. Based on this, this paper proposes an OJ exercise recommendation model based on deep reinforcement learning and program analysis. Firstly, analyzing optimal solution of exercises to mine correlations between exercises. Then, comparing the similarity between programs submitted by students and optimal solution of exercises to check actual correctness of the programs submitted by students, so that knowledge state of students can be estimated more accurately. Finally, using deep reinforcement learning technology, taking knowledge tracking model as Student simulator and trea-ting student simulator's performance difference on ali the exercises before and after answering exercises provided by exercise recommendation model as reward, so that exercise recommendation model can learn which exercise is able to improve the students' programming ability to the greatest extent-and recommend such exercises to students. This paper conducts extensive experiments on two datasets CodeForces and Libre of the well-known OJ system-and experimental results show that the proposed model can achieve higher performance than the state-of-the-art recommendation models.