Intelligent Recommendation of Open Educational Resources: Building a Recommendation Model Based on Deep Neural Networks

Abstract

Information overload is a challenge for the development of online education. To address the problem of intelligent recommendation of educational resources, the study proposes an intelligent recommendation model of educational resources based on deep neural networks. First, a deep neural network-based custom recommendation model for educational resources is constructed after a multilayer perceptron-based prediction model is established. The results showed that the prediction model proposed in the study steadily reduced the average absolute error as the number of iterations increase, reaching an average of 0.704, with the loss value stabilising at around 0.6, which is lower than that of the deep neural network prediction model. Compared to the deep neural network prediction model, the normalised discounted cumulative gain is typically 0.01 higher and in terms of hit rate, 0.03 higher. The prediction time of the similarity algorithm is faster than that of the neural network. The mean squared error ranged from a high of 1.29 to a low of 1.19, both lower than other algorithms, and the mean absolute error ranged from a high of 0.56 to a low of 0.54, lower than all other algorithms except the support vector machine algorithm. The average absolute error of the deep neural network resource representation algorithm ranged from a high of 1.46 to a low of 1.45, lower than all other algorithms except the support vector machine algorithm, and the average squared error ranged from a high of 3.43 to a low of 3.24, better than all other algorithms. In conclusion, the model constructed by the study has a good application effect in recommending educational resources, and has a certain promoting effect on the development of online education.