LiveGraph: 基于主动-结构神经重排序的运动推荐
LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation
AI总结：
LiveGraph通过图表示增强策略和动态重排序机制，解决学生参与长尾分布和个性化学习轨迹适应问题，提升推荐精度与教学多样性。
摘要：
数字学习环境的持续扩展推动了智能系统提供个性化教育内容的需求。尽管当前运动推荐框架已取得显著进展，但经常面临学生参与长尾分布和无法适应个性化学习轨迹的挑战。我们提出了LiveGraph，一种新颖的主动-结构神经重排序框架，旨在克服这些限制。我们的方法利用基于图的表示增强策略，弥合主动和非活跃学生之间的信息差距，同时整合动态重排序机制以促进内容多样性。通过优先考虑学习历史中的结构关系，所提出的模型有效平衡了推荐精度与教学多样性。在多个真实世界数据集上的全面实验评估表明，LiveGraph在预测准确性和运动多样性方面均优于当前基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
