A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging correctly choose course content from among many resources due differences in users’ knowledge structures. Therefore, a recommender system essential role of improving learning efficiency users. At present, platforms have built diverse systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite development and contributions based on CF, deep models personalized recommendation are being studied because problems sparsity scalability. solve problems, this study proposes novel learning-based (DECOR), which elaborately captures high-level user behaviors attribute features. The DECOR model can reduce information overload, high-dimensional achieve high feature extraction performance. We perform several experiments utilizing real-world datasets evaluate model’s performance compared with approaches. experimental results indicate offers better more robust than methods.