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

arXiv: https://arxiv.org/abs/2602.17036 · HTML: https://mlsanity.com/paper/2602.17036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 73 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 29 of 29)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing](https://mlsanity.com/paper/2302.06881/) (2023)
- [Probing Classifiers are Unreliable for Concept Removal and Detection](https://mlsanity.com/paper/2207.04153/) (2022)
- [VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs](https://mlsanity.com/paper/2308.02117/) (2023)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [LTE4G: Long-Tail Experts for Graph Neural Networks](https://mlsanity.com/paper/2208.10205/) (2022)
- [Generative Contrastive Graph Learning for Recommendation](https://mlsanity.com/paper/2307.05100/) (2023)
- [Enhancing Recommendation Diversity by Re-ranking with Large Language Models](https://mlsanity.com/paper/2401.11506/) (2024)
- [CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer](https://mlsanity.com/paper/2406.10296/) (2024)
- [SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection](https://mlsanity.com/paper/2405.11225/) (2024)
- [Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand](https://mlsanity.com/paper/2202.07147/) (2022)
- [A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning](https://mlsanity.com/paper/2112.15400/) (2021)
- [Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models](https://mlsanity.com/paper/2407.18999/) (2024)
- [Curricular Subgoals for Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.08232/) (2023)
- [Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts](https://mlsanity.com/paper/2308.16609/) (2023)
- [NR4DER: Neural Re-ranking for Diversified Exercise Recommendation](https://mlsanity.com/paper/2506.06341/) (2025)
- [Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery](https://mlsanity.com/paper/2506.00989/) (2025)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning](https://mlsanity.com/paper/2502.00305/) (2025)
- [GraphMASAL: A Graph-based Multi-Agent System for Adaptive Learning](https://mlsanity.com/paper/2511.11035/) (2025)
- [Introducing Variational Autoencoders to High School Students](https://mlsanity.com/paper/2111.07036/) (2021)
- [On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies](https://mlsanity.com/paper/2209.10072/) (2022)
- [Dynamic Knowledge Selector and Evaluator for recommendation with Knowledge Graph](https://mlsanity.com/paper/2502.15623/) (2025)
- [Hierarchical Uncertainty-Aware Graph Neural Network](https://mlsanity.com/paper/2504.19820/) (2025)
- [IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder](https://mlsanity.com/paper/2511.22116/) (2025)

## Built on by (most cited first, 1 of 1)

- [LightAIR: Lightweight Action Inversion and Riemannian Rectification for Text-based Person Anomaly Search](https://mlsanity.com/paper/2608.09152/) (2026)

## Abstract (arXiv)

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.