LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation
This work addresses the need for more effective and diverse exercise recommendations for students in digital learning environments, representing an incremental improvement over existing methods.
The paper tackled the problem of personalized exercise recommendation in digital learning by addressing long-tailed student engagement and adapting to individual learning trajectories, resulting in LiveGraph, which outperformed baselines in predictive accuracy and exercise diversity on real-world datasets.
The continuous expansion of digital learning environments has catalyzed the demand for intelligent systems capable of providing personalized educational content. While current exercise recommendation frameworks have made significant strides, they frequently encounter obstacles regarding the long-tailed distribution of student engagement and the failure to adapt to idiosyncratic learning trajectories. We present LiveGraph, a novel active-structure neural re-ranking framework designed to overcome these limitations. Our approach utilizes a graph-based representation enhancement strategy to bridge the information gap between active and inactive students while integrating a dynamic re-ranking mechanism to foster content diversity. By prioritizing the structural relationships within learning histories, the proposed model effectively balances recommendation precision with pedagogical variety. Comprehensive experimental evaluations conducted on multiple real-world datasets demonstrate that LiveGraph surpasses contemporary baselines in both predictive accuracy and the breadth of exercise diversity.