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<p>Dynamic State Tracking (DST) is pivotal for personalized recommender systems and user modeling, aiming to estimate users’ evolving latent states from sequential interactions. However, existing deep sequence paradigms predominantly treat interaction entities, such as semantic tokens and items, as isolated deterministic vectors. This approach often overlooks latent structural dependencies among entities, including knowledge graph topologies, and remains limited in quantifying the epistemic uncertainty inherent in stochastic user behaviors caused by random interactions and aleatoric noise. To address these dual challenges, we propose Adaptive G-UKT (Adaptive Graph-Enhanced Uncertainty-aware Knowledge Tracing), a unified probabilistic framework for temporal sequence modeling. Unlike traditional point-estimation models, we map hidden user states into Gaussian distributions, enabling the simultaneous tracking of semantic activation levels and estimation confidence through diagonal covariance. To mitigate data sparsity, we design an Adaptive Graph Learner that autonomously infers latent semantic correlations from raw data, coupled with an Adaptive Gaussian-HGNN that propagates uncertainty information across the dynamically learned topology. Furthermore, we introduce a Wasserstein attention mechanism to perform distribution-aware sequence retrieval and an uncertainty-guided contrastive learning strategy to enhance model robustness against noisy interactions. Extensive experiments on four large-scale real-world sequential datasets, namely ASSISTments2009, Bridge2Algebra2006, Algebra2005, and NIPS34, demonstrate that Adaptive G-UKT achieves competitive performance against state-of-the-art baselines, showing particularly significant gains in sparse data regimes. Crucially, visualization analysis confirms the model’s capacity to autonomously uncover intrinsic structural topologies, bridging the critical gap between high-precision deep sequence learning and interpretable knowledge graph reasoning.</p>

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Adaptive G-UKT: a unified probabilistic framework for knowledge tracing via adaptive graph topology learning and uncertainty-aware Gaussian embeddings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Nan Jia,
  • Weitao Su,
  • Junrui Xian,
  • Shijia Zou,
  • Yixue Xia

摘要

Dynamic State Tracking (DST) is pivotal for personalized recommender systems and user modeling, aiming to estimate users’ evolving latent states from sequential interactions. However, existing deep sequence paradigms predominantly treat interaction entities, such as semantic tokens and items, as isolated deterministic vectors. This approach often overlooks latent structural dependencies among entities, including knowledge graph topologies, and remains limited in quantifying the epistemic uncertainty inherent in stochastic user behaviors caused by random interactions and aleatoric noise. To address these dual challenges, we propose Adaptive G-UKT (Adaptive Graph-Enhanced Uncertainty-aware Knowledge Tracing), a unified probabilistic framework for temporal sequence modeling. Unlike traditional point-estimation models, we map hidden user states into Gaussian distributions, enabling the simultaneous tracking of semantic activation levels and estimation confidence through diagonal covariance. To mitigate data sparsity, we design an Adaptive Graph Learner that autonomously infers latent semantic correlations from raw data, coupled with an Adaptive Gaussian-HGNN that propagates uncertainty information across the dynamically learned topology. Furthermore, we introduce a Wasserstein attention mechanism to perform distribution-aware sequence retrieval and an uncertainty-guided contrastive learning strategy to enhance model robustness against noisy interactions. Extensive experiments on four large-scale real-world sequential datasets, namely ASSISTments2009, Bridge2Algebra2006, Algebra2005, and NIPS34, demonstrate that Adaptive G-UKT achieves competitive performance against state-of-the-art baselines, showing particularly significant gains in sparse data regimes. Crucially, visualization analysis confirms the model’s capacity to autonomously uncover intrinsic structural topologies, bridging the critical gap between high-precision deep sequence learning and interpretable knowledge graph reasoning.