关键词: MOOC推荐, 文本卷积神经网络, 双向长短时记忆网络, 注意力机制, 关联聚类分析, 概率矩阵分解

Abstract: In order to make full use of the contextual information of MOOC（massive open online course） and accurately represent the feature of learners and courses, a multi-feature fusion based model for MOOC recommendation（MFF-MOOCREC） is proposed. Text convolutional neural network as well as bidirectional long short-term memory network are introduced respectively to capture the textual and sequential feature from data, and a multi-level attention mechanism is designed to extract key information from learning records, review texts and multiple attributes of course. In order to increase the coverage ratio of recommendation, prefix-projected pattern growth and affinity propagation algorithm are adopted in combination for a relevant clustering analysis on the original courses’ category labels. Probabilistic matrix factorization is used for parameters training, and the predicted ratings are obtained from the dot product between learners’ latent vectors and courses’ latent vectors. Experiments show that, compared with the available methods, MFF-MOOCREC achieves the improvements of average 46.86%, 41.19%, 10.95% and 44.08%, 28.79%, 9.81% on hit ratio, normalized discounted cumulative gain and coverage ratio indicators over Coursera dataset and iCourse dataset respectively, which indicates the proposed model can effectively alleviate the problem of data sparseness as well as improve the performance of recommendation to some extent.

Key words: MOOC recommendation, text convolutional neural network, bidirectional long short-term memory network, attention mechanism, relevant clustering analysis, probabilistic matrix factorization