Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Abstract

Aiming at the problems of low accuracy and large limitations of the current personalized course recommendation method in the educational big data environment, a personalized course recommendation method based on learner interest mining in the educational big data environment is proposed. First, a corresponding online course recommendation model framework is proposed by adopting GRU, which can effectively solve the problems of gradient disappearance and gradient explosion in the process of training the RNN neural network. Then, by introducing an auto-regressive language model, XLNet (Generalized Autoregressive Pretraining for Language Understanding), the information missing problem under the Mask mechanism in the BERT model is effectively optimized, and bidirectional prediction is achieved. Finally, by introducing a temporal attention mechanism into the model, enough attention is assigned to highlight local important information on key information, which improves the quality of hidden layer feature extraction, and a high-accuracy personalized course recommendation based on learner interest mining is realized. The proposed algorithm is compared with the other three collaborative filtering algorithms and the RNN algorithm through simulation experiments. The results show that the precision, recall, and F1-measure of the proposed algorithm in the personalized course recommendation results for different types of courses under the condition of the same database are all optimal. The largest values were 92.1%, 89.3%, and 90.7%, respectively. The overall performance is better than other comparison algorithms. This method can improve the accuracy and optimization limitations of personalized courses and can fully tap the interests of learners. It is of great significance for learners to choose personalized courses in the current educational big data environment.