将缺失数据视为城市信号，提升租金预测准确率
MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction
- 把数据缺失当作城市上下文信号，分三阶段建模
- 悉尼和纽约数据上，误差比最佳基线降低超50%
- 适合处理不完整多模态城市数据的场景
多模态城市数据拓展了区域表征学习的应用，如功能区识别和房地产评估，但也带来了数据缺失的挑战。现有方法通常通过插补处理缺失数据，将缺失视为噪声，忽略了其潜在语义价值。为此，我们提出 MARCUS，一种将缺失性视为上下文城市信号的区域表征模型。MCUS 分三阶段建模：内部学习联合编码可观测特征与缺失模式；跨模态学习估计模态可靠性以指导跨模态交互；融合阶段使用缺失感知与时序感知门控生成最终区域嵌入。我们将 MARCUS 应用于具有长期趋势和季节波动的租金预测任务，使用来自悉尼和纽约的真实数据集。实验结果表明，MARCUS 达到当前最优性能，在悉尼上将 MAE 降低 51.35%，在纽约上降低 12.62%。额外实验（包括基于插补的消融研究和随机增加缺失性分析）进一步验证了该方法的有效性。
原文摘要 · Abstract (English)
Multimodal urban data has expanded the applications of urban region representation learning, such as functional zone identification and real estate appraisal, but also introduces challenges caused by data incompleteness. Existing studies usually handle missing data through imputation, treating missingness as noise while ignoring its potential semantic value. To address this issue, we propose MARCUS, a missing-aware region representation model that treats missingness as a contextual urban signal. MARCUS models missingness in three stages: Intra Learning jointly encodes observed features and missing patterns, Inter Learning estimates modality reliability to guide cross-modal interaction, and Fusion uses missing-aware and time-aware gating to generate the final region embedding. We apply MARCUS to rent prediction, a task with long-term trends and seasonal fluctuations, using real-world datasets from Sydney and New York. Experimental results show that MARCUS achieves state-of-the-art performance, reducing MAE by 51.35% on Sydney and 12.62% on New York compared with the best baselines. Additional experiments, including an imputation-based ablation study and randomized additional-missingness analysis, further demonstrate the effectiveness of the proposed method.
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