LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation

2026-02-18

AI Summary

This work proposes a neural reranking framework that integrates graph structure enhancement with dynamic reranking to address performance bottlenecks in student exercise recommendation, which arise from the long-tailed distribution of student engagement and the high degree of personalization in learning paths. By actively capturing structural relationships inherent in learning behaviors, the method constructs structure-aware graph representations and incorporates a dynamic reranking mechanism to effectively mitigate the information gap between active and less-active students. Extensive experiments on multiple real-world educational datasets demonstrate that the proposed framework significantly outperforms existing baselines in both recommendation accuracy and content diversity, thereby validating its effectiveness and innovation in personalized educational recommendation.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• exercise recommendation
• long-tailed distribution
• learning trajectories
• personalized education
• student engagement

Innovation

• graph-based representation
• neural re-ranking
• exercise recommendation
• learning trajectory
• content diversity