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.

翻译：数字学习环境的持续扩展推动了对能够提供个性化教育内容的智能系统的需求。尽管当前习题推荐框架已取得显著进展，但其仍常面临学生参与度的长尾分布问题，且难以适应个体化的学习轨迹。本文提出LiveGraph——一种新颖的主动结构神经重排序框架，旨在克服这些局限。该方法采用基于图的表示增强策略，弥合活跃与非活跃学生间的信息鸿沟，同时集成动态重排序机制以促进内容多样性。通过优先学习历史中的结构关系，所提模型有效平衡了推荐精度与教学多样性。在多个真实数据集上进行的综合实验评估表明，LiveGraph在预测准确性和习题多样性广度方面均优于现有基线模型。