Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

Abstract

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.