Course video recommendation with multimodal information in online learning platforms: A deep learning framework
Wei Xu, Yuhan Zhou
Abstract
Abstract With the rapid development of online learning platforms, learners have more access to various kinds of courses. However, they may find it difficult to make choices due to the massive number of courses. The main contribution of our research is the design of a course recommendation framework which extracts multimodal course features based on deep learning models. In this framework, different kinds of information of course, such as course title, and course audio and course comments, are used to make proper recommendation in online learning platforms. Moreover, we utilize both explicit and implicit feedback to infer learner’s preference. Based on real‐world datasets, our empirical results show that the proposed framework performs well in course recommendation, achieving an AUC score of 79.03%. This framework can provide technical support for course video recommendation, thus helping online learning platforms to manage course resources and optimize user learning experience.