【摘要】 为解决在线学习当中，学习者行为的数量远少于在线课程的样本总数所产生的数据稀疏问题，提出一种基于多任务自编码器的课程推荐模型（multi-task autoencoder course recommendation model,MAEM）。通过分析学习者的学习行为，将总体任务分为两个子任务：任务一是学习者浏览课程章节列表行为，任务二是完成课程50%的学习行为，通过共享网络底部的隐藏层提高泛化能力。模型总体划分为共享嵌入、自编码器与分解预测、任务组合3个模块，3个模块协同工作，旨在突破训练数据稀疏问题。将MAEM与7种常用的推荐算法比较，实验结果表明，MAEM算法优于7种热门的推荐算法，验证了其在课程推荐中的有效性。更多还原

【Abstract】 There is a big difference of the amount of data between learners’ behaviors and the courses on online education.To solve the problem of data sparsity,a multi-task autoencoder course recommendation model(MAEM)was proposed.By making full use of the behavior characteristics of students’ online learning,the overall task was divided into two sub-tasks.The first task was the behavior of browsing the list of course chapters,and the second task was the behavior of finishing 50% of the course.These two sub-tasks were sharing with the hidden layer at the bottom of the network to improve the generalization ability.The model was generally divided into three modules including shared embedding,decomposition prediction and autoencoder,and task combination.These three modules worked together to break through the problem of sparse training data.Through the experiments,the proposed model outperforms seven state-of-the-art ones and provides more accurate course recommendations for students.更多还原