Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Abstract

Aiming at the problems of difficult-to-extract effective information and insufficient feature extraction in the existing intelligent question answering robot environment, a personalized course resource recommendation algorithm based on deep learning is proposed. Firstly, the potential preferences of users are obtained through course-related data. Secondly, the authors use one-hot coding and embedding to convert word vectors into low-dimensional, dense real-valued vectors and input them into the CIN-GRU model. Finally, the attention mechanism is used to improve the attention of some words and the accuracy of personalized course recommendation. The experiment shows that when the recommended list is 35, the precision, recall, and F1 value of the proposed personalized course recommendation method are 0.862, 0.851, and 0.857, respectively, which are higher than those of the comparison method. Therefore, the performance of the proposed method in sustainable personalized course resource recommendation is better than that of the comparison method.