MRTI-CR: A model based on multi-relationship and time-aware interest for personalized course recommendation

Abstract

With the advancement of information technology, Massive Open Online Course (MOOC) platforms offer students a diverse selection of courses but also introduce the challenge of “course overload”. Most existing course recommendation methods primarily model students’ interactions with courses implicitly, overlooking the rich multi-relationships between different entities and also failing to account for the impact of students’ evolving learning interests, particularly the influence of time on course selection behavior. To address these limitations, we propose a model based on Multi-Relationship and Time-aware Interest for personalized Course Recommendation(MRTI-CR), which effectively integrates heterogeneous relationships and dynamic interest evolution. Our approach extracts global features of users and courses by constructing a heterogeneous information network and leveraging a meta-path-guided graph convolutional network, such as prerequisite relationship meta-paths. Furthermore, to enhance the utilization of temporal information, we design a dynamic interest extraction module based on a time-aware Transformer. This module incorporates time-interval-aware positional encoding and optimizes multi-head attention using temporal weights, enabling the dynamic modeling of students’ learning interests. Experiments conducted on the MOOCCube public dataset demonstrate that MRTI-CR outperforms existing baseline models across multiple evaluation metrics in the course recommendation task.