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arXiv Machine Learning By Chenya Huang, Bin Liang, Zhidong Li, Yuxi Lu, Kunqi Li, Justin Wang, Fang Chen

MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction

Read the original on arXiv Machine Learning →

The paper introduces MARCUS, a missing‑aware region representation model that treats missing data as a contextual urban signal rather than noise. MARCUS operates in three stages—Intra Learning, Inter Learning, and Fusion—to encode observed features, assess modality reliability, and generate final embeddings. Applied to rent prediction in Sydney and New York, MARCUS outperforms baselines, reducing MAE by 51.35% and 12.62% respectively, and additional experiments confirm its effectiveness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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