CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view

Abstract

Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner’s personal information and the courses’ teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics.