Self-Supervised pre-training model based on Multi-view for MOOC Recommendation

Recommendation strategies based on concepts of knowledge are gradually applied to personalized course recommendation to promote model learning from implicit feedback data. However, existing approaches typically overlook the prerequisite dependency between concepts, which is the significant basis for connecting courses, and they fail to effectively model the relationship between items and attributes of courses, leading to inadequate capturing of associations between data and ineffective integration of implicit semantics into sequence representations. In this paper, we propose S elf-Supervised pre-training model based on M ulti-view for M OOC R ecommendation (SSM4MR) that exploits non-explicit but inherently correlated features to guide the representation learning of users' course preferences. In particular, to keep the model from relying solely on course prediction loss and overmphasising on the final performance, we treat the concepts of knowledge, course items and learning paths as different views, then sufficiently model the intrinsic relevance among multi-view through formulating multiple specific self-supervised objectives. As such, our model enhances the sequence representation and ultimately achieves high-performance course recommendation. All the extensive experiments and analyses provide persuasive support for the superiority of the model design and the recommendation results.