用可解释的机器学习建模视频体验质量，提升预测准确率与透明度。
TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data
- 基于频域特征与可解释的KAN网络构建时序模型
- 在主流数据集上实现更高精度的用户体验预测
- 适合需要模型可解释性的流媒体优化场景
用户体验质量（QoE）建模对优化视频流媒体服务至关重要，能捕捉不同特征与用户感受之间的复杂关系。本文提出一种新型可解释机器学习方法，直接处理原始时序数据，结合柯尔莫戈洛夫-阿诺德网络（KAN）作为可解释读出层，作用于紧凑的频域特征，既保留时间信息又确保模型透明可解释。我们在多个主流数据集上验证该方法，结果表明其在QoE预测中具有更高的准确性，同时提供良好的可解释性。
原文摘要 · Abstract (English)
Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in video streaming applications using interpretable Machine Learning (ML) techniques over raw time series data. Unlike traditional black-box approaches, our method combines Kolmogorov-Arnold Networks (KANs) as an interpretable readout on top of compact frequency-domain features, allowing us to capture temporal information while retaining a transparent and explainable model. We evaluate our method on popular datasets and demonstrate its enhanced accuracy in QoE prediction, while offering transparency and interpretability.
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