基于深度学习的课程推荐模型

针对网络课程推荐中数据稀疏和推荐效果不佳的问题，将深度学习引入课程推荐，提出基于辅助信息的神经网络模型（IUNeu）. 该模型在已有神经矩阵分解模型（NeuMF）的基础上，结合用户信息和课程信息，并考虑它们之间的相互作用关系，以提升模型表示用户和课程的准确性. 爬取慕课网（MOOC）上的学习数据进行实验，结果表明，随着向量长度和推荐课程数的增加，IUNeu模型的性能增长速度较NeuMF模型更快；不同的消极采样量对2个模型的影响较大，模型性能随着消极采样量的增加而增加，当采样量达到一定值时，变化趋于稳定；IUNeu模型比NeuMF模型具有更高的收敛速度. 在IUNeu模型中加入更多课程特征信息，可以进一步提高IUNeu模型的推荐质量.

Deep learning based course recommendation model

Deep learning was introduced into course recommendation and a neural network model based on assistant information, item and user information (IUNeu) was proposed, aiming at the problem of sparse data and poor recommendation effect at online course recommendation. Based on the existing neural matrix factorization (NeuMF) model with integrated user and course information, the interaction between them was considered to improve the model accuracy of users and courses. Experiments were conducted by crawling the learning data on massive open online course (MOOC) online learning platform. Results showed that with the increase of vector length and the number of recommended courses, the performance of the IUNeu model increased faster than that of the NeuMF model. Different sampling quantities had a great impact on both two models. The models’ performance increased with the increasing sampling quantities, and when the sampling quantity reached a specific threshold, the performance became stable. The convergence rate of the IUNeu model was higher than that of the NeuMF model. The experimental results show that the recommendation quality can be further improved by adding more course feature information to the IUNeu model.