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

中文翻译：

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UniNet：使用深度学习的下一学期课程推荐

课程注册推荐是一项相关任务，可帮助大学生决定下学期注册的最佳课程组合。特别是，已经开发了诸如矩阵分解和协同过滤之类的推荐系统技术来尝试解决这个问题。由于这些技术无法表示学业成绩数据集的时间依赖性，我们提出了一种使用递归神经网络的深度学习方法，旨在更好地表示课程成绩的时间顺序如何影响成功概率。我们已经表明，仅使用成绩信息就可以在 AUC 指标上获得 81.10% 的性能，并且可以开发具有学术学生表现预测的推荐系统。