关键词: 智能推荐, 课程序列, 深度学习, 长短时记忆（LSTM）网络, 数据挖掘

Abstract: There are a large number of online learning courses and there is a clear problem of information overload. One of the most effective ways to solve the problem of information overload is to use personalized recommendation system. According to the characteristics that the learners’ courses are often time-sequential, an online course recommendation model based on LSTM (long short-term memory) network is proposed. The characteristics of learning behaviors are extracted from the sequence of lessons learned by a large number of learners, thereby predicting the courses that the learner will learn. The algorithm proposed in this paper is based on the time sequence between courses. Therefore, according to the closeness of the relationship between courses, the accuracy of the recommendation after course classification is higher. Due to the continuous update of online courses, the workload of manually maintaining the course classification is large, and the classification is not scientific enough. This paper uses GSP (generalized sequential pattern mining algorithm) and spectral clustering algorithm to discover the hidden time linkage between courses and proposes a more reasonable course classification method. Compared with the traditional collaborative filtering algorithm and the course recommendation algorithm based on RNN (recurrent neural network), experimental results show that the accuracy of the proposed algorithm is higher.

Key words: intelligent recommendation, course sequence, deep learning, long short-term memory (LSTM) network, data mining