What learns next: Learning intents guided dual contrastive learning model for online course recommendation

Abstract

Course recommender systems play a crucial role in assisting learners from diverse backgrounds in selecting suitable courses from various options to achieve their educational pursuits. However, these systems face challenges in accuracy and robustness due to partial learning preference modeling and unconventional long sequences sparsity. This paper proposes a learning intents guided dual contrastive learning model for online course recommendation to address these issues. Grounded in self-determination theory, the proposed model incorporates both intrinsic motivation (long-term learning intent) and extrinsic motivation (short-term learning intent) to characterize learners’ decision-making processes. Additionally, a dual contrastive learning module, comprising self-contrastive learning and last-course guided supervised contrastive learning, is designed to mitigate the sparsity issue and enhance the robustness of sequence representation learning. Extensive experiments demonstrate the effectiveness of the model compared to several state-of-the-art methods. Furthermore, the model exhibits excellent performance for learners with varying learning sequences.