Personalized Curriculum Recommendation Model Based on Deep Learning in Data-driven Mode
The former,as the mainstream service mode of large-scale data,had become the focus of personalized course recommendation research.In order to effectively improve the coverage and accuracy of course recommendation,the deep learning algorithm was used to train the multi-dimensional features of learning users and online courses,and the recom-mendation efficiency was optimized by parallel computing.Firstly,the behavior records of online courses and learning us-ers were obtained,and the key features were extracted by PCA algorithm;Then,according to the key features,the course scoring function was constructed,and regular items were introduced to improve the coverage of course recommendation;Then,the recurrent neural network(RNN)algorithm was used to train learner-curriculum characteristics.Through the cyclic superposition of hidden layers of historical time series,the influence of historical time series samples on the current moment was integrated to improve the accuracy of personalized curriculum recommendation.By setting the RNN scale and the feature quantity involved in the operation differently,the personalized course recommendation simulation of five differ-ent online learning platforms was carried out.The results showed that the coverage and accuracy of the RNN personalized course recommendation model for the TOP-10 recommendation of five online learning platforms were above 80％,and the stability was high.
万方数据
维普
