Online Teaching Course Recommendation Based on Autoencoder

Abstract

When using traditional recommendation algorithms to solve the problems of course recommendation, such as data sparseness and cold start, the performance of recommendation cannot be significantly improved. In order to solve its limitations in capturing learners’ preferences and the characteristics of courses, this paper first clarifies the research foundation of course recommendation based on autoencoder and analyzes the description of course relevance and recommendation methods. According to the timing characteristics of online learning, an online course recommendation model based on autoencoder is proposed where the long-term and short-term memory (LSTM) network is used to improve the autoencoder, so that it can extract the temporal characteristics of data. Then, the Softmax function is used to recommend courses. The experimental results show that, compared with recommendation model of collaborative filtering algorithm and traditional autoencoder, the proposed method has higher recommendation accuracy.