发表机构
The Property Investors Alliance（房地产投资者联盟）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市区域表示中缺失数据的语义价值被忽略问题，提出缺失感知区域表示模型MARCUS，将缺失视为城市信号，在悉尼和纽约租金预测任务上较基线显著降低MAE，性能最优。
AI 中文摘要
多模态城市数据拓展了城市区域表示学习的应用，如功能区识别与房地产估价，但也带来了数据不完整引发的挑战。现有研究通常通过插补处理缺失数据，将缺失视为噪声，忽略其潜在语义价值。为解决该问题，本文提出MARCUS，一种将缺失视为上下文城市信号的缺失感知区域表示模型。MARCUS分三个阶段建模缺失性：内部学习联合编码观测特征与缺失模式，交互学习估计模态可靠性以指导跨模态交互，融合模块采用缺失感知与时序门控生成最终区域嵌入。本文将MARCUS应用于具有长期趋势与季节波动的租金预测任务，使用悉尼与纽约的真实世界数据集。实验结果显示，MARCUS实现了最优性能，与最佳基线相比，悉尼数据集上平均绝对误差（MAE）降低51.35%，纽约数据集上降低12.62%。额外实验包括基于插补的 ablation 研究与随机额外缺失分析，进一步验证了所提方法的有效性。
英文摘要
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.
Comments10 pages, 7 figures, 4 tables. Accepted at the 2026 IEEE International Conference on Data Mining (ICDM 2026)
