论文标题
UNINET：使用深度学习的下一个学期课程建议
UniNet: Next Term Course Recommendation using Deep Learning
论文作者
论文摘要
课程入学建议是一项相关任务，可以帮助大学生决定下一学期注册课程的最佳组合。特别是，已开发了推荐的系统技术（例如矩阵分解和协作过滤）来解决此问题。由于这些技术无法代表学术绩效数据集的时间依赖性性质，我们提出了一种使用经常性神经网络的深度学习方法，旨在更好地代表时间顺序的课程等级如何影响成功的可能性。我们已经表明，只有一级信息就可以在AUC度量上获得81.10％的性能，并且可以开发具有学术学生绩效预测的推荐系统。在不同的学生GPA级别和课程困难中，这表明这是有意义的
Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance prediction. This is shown to be meaningful across different student GPA levels and course difficulties
