# UME: A Unified Meta-Generalization Framework for Cross-Domain ETA

arXiv: https://arxiv.org/abs/2606.00979 · HTML: https://mlsanity.com/paper/2606.00979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 16 of 16)

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- [Heterogeneous Domain Generalization via Domain Mixup](https://mlsanity.com/paper/2009.05448/) (2020)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization](https://mlsanity.com/paper/2310.19795/) (2023)
- [A Unified Perspective on Multi-Domain and Multi-Task Learning](https://mlsanity.com/paper/1412.7489/) (2014)
- [Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](https://mlsanity.com/paper/2201.06814/) (2022)
- [Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce](https://mlsanity.com/paper/2012.08952/) (2020)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction](https://mlsanity.com/paper/2303.06095/) (2023)
- [Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services](https://mlsanity.com/paper/2505.00375/) (2025)
- [DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions](https://mlsanity.com/paper/2505.00402/) (2025)

## Abstract (arXiv)

Accurate Estimated Time of Arrival (ETA) prediction on checkout page is crucial in instant logistics for enhancing user satisfaction, optimizing dispatching, and controlling operational costs. In international on-demand delivery platforms, where ETA data originates from diverse countries or regions with different patterns, multi-domain modeling is of great importance and has been widely adopted. However, existing methods still face three critical challenges in real-world deployment. First, current multi-domain models struggle to generalize to completely unseen domains, failing to achieve zero-shot prediction during the initial cold-start phase. Second, cross-domain feature spaces are often assumed to be consistent, whereas new domains commonly suffer from structural missingness of offline (statistical) features due to the lack of historical data. Third, such feature missingness often compels industrial systems to model mature and cold-start domains separately, hindering knowledge transfer and increasing maintenance overhead. To address these challenges, we propose UME, a Unified Meta-generalization framework for ETA. Specifically, UME integrates a unified dual-branch architecture with a novel meta-learning mechanism that employs a hypernetwork-based meta learner. By leveraging domain-level knowledge and instance-level context, the meta learner empowers three meta modules to dynamically modulate feature gating, expert attention, and final prediction, capturing cross-domain correlations and facilitating intra-domain adaptation. A knowledge distillation strategy is further introduce to enhance performance. UME has now been deployed in Meituan-keeta delivery platform (the largest international food delivery platform in China). Extensive offline experiments and online A/B tests demonstrate that UME significantly outperforms existing baselines.