提出新算法，更准地估计政治立场，尤其擅长处理回答稀疏的用户。
IXPLORE: Bounded Ideal Point Estimation with Grid-Based Uncertainty Quantification
- 结合预测精度与稀疏性感知似然，优化理想点估计
- 在五大数据集上重建与补全误差更低，稀疏用户表现尤佳
- 采用网格后验推断量化不确定性，结果可解释性强
理想点估计广泛用于政治数据分析与可视化。然而，选择空间模型存在权衡：基于效用函数的模型（如项目反应理论）不以预测准确率为优化目标，而大多数机器学习方法在嵌入稀疏测试响应时难以泛化。我们提出IXPLORE，一种结合预测拟合目标与稀疏感知似然函数的有界理想点估计算法。在涵盖问卷、投票记录和讨论数据的五个基准数据集上，该方法在重建与补全误差上优于模型与机器学习基线，尤其在用户响应稀疏时表现更优。此外，非线性特征变换可进一步降低重建误差，同时保持可解释性。为量化不确定性，IXPLORE在有界二维隐空间上应用网格后验推断。该方法已作为PyPI Python包发布，提供灵活框架，支持快速推理与强补全性能，适用于构建有界、可解释的政治地图。
原文摘要 · Abstract (English)
Ideal point estimation is widely used to analyze and visualize political data. However, selecting the corresponding spatial model involves various trade-offs: while model-based approaches such as Item Response Theory (IRT) are based on utility functions rather than optimized for predictive accuracy, most Machine Learning (ML) alternatives struggle to generalize beyond training data when embedding sparse test responses. We introduce IXPLORE, a bounded ideal point estimation algorithm that combines a predictive fit objective with a sparsity-aware likelihood function. On five benchmark datasets spanning surveys, roll calls, and deliberation, this approach surpasses model-based and ML-based algorithms on reconstruction and imputation error - especially for users with sparse responses. Furthermore, we show that non-linear feature transforms can further reduce the reconstruction error while remaining visually interpretable. To quantify uncertainty, IXPLORE applies grid-based posterior inference on a bounded 2D latent space. Available as a Python package on PyPI, IXPLORE offers a flexible framework for constructing bounded, interpretable political maps with fast inference and strong imputation performance.
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