A Multi-User Collaborative Recommendation Mechanism for Career Planning in Online Learning Over Edge Networks

Abstract

In recent years, online learning platforms have gained popularity, particularly in the realm of career planning and skill development. However, most existing recommendation systems fail to fully integrate multi-behavioral user data and collaborative group preferences. This paper presents a Multi-User Collaborative Recommendation Mechanism for Career Planning in Online Learning (MCR-MCL), which combines multi-behavioral interaction data, group consensus modeling, and edge Networks to enhance personalized career planning recommendations. By leveraging edge network deployment, our system enables low-latency, localized updates that dynamically adapt to users' behaviors without frequent reliance on centralized cloud servers. We propose an innovative approach that leverages Graph Convolutional Networks (GCNs) to process user-item interactions and a behavioral independence modeling mechanism to avoid over-reliance on a single interaction type. We evaluate the effectiveness of the proposed mechanism using two real-world datasets—CareerMOOC and CareerEdNet—and demonstrate that our model significantly outperforms existing state-of-the-art methods in terms of recommendation accuracy, diversity, and low-latency adaptability through edge-based processing. The experimental results indicate that MCR-MCL can provide highly relevant, diverse, and dynamic recommendations that are essential for career planning in the context of online learning.