1. School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China 2. Computer and Information Engineering College, Zhejiang Gongshang University, Hangzhou 310018, China
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.
Fig.3Influence of vector length on model performance
Fig.4Influence of number of recommended courses on model performance
Fig.5Influence of negative sample number on model performance
Fig.6Influence of different iterations on model performance
Fig.7Influence of course characteristics on model performance
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