Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

中文翻译：

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MetaStore：一种任务自适应元学习模型，用于具有多城市知识转移的最佳商店布局

最佳店铺布局旨在通过从大规模城市数据中分析和挖掘用户偏好来确定新实体店的最佳位置，从而最大限度地提高销售额。近年来，连锁企业在新城市的扩张带来了一些挑战，主要有两个方面：（1）新城市的数据稀缺，因此大多数现有模型往往不起作用（即过度拟合），因为这些工作的卓越性能取决于大规模训练样本；(2)不同城市之间的数据分布差异，因此，从其他城市学到的知识不能直接用于新城市。在本文中，我们提出了一个任务自适应模型无关的元学习框架，即 MetaStore，通过转移从多个学习到的先验知识来解决这两个挑战，并在数据不足的新城市中提高预测性能以实现最佳商店布局数据丰富的城市。具体来说，我们开发了一种任务自适应元学习算法来学习来自多个城市的特定城市的先验初始化，与其他方法相比，该算法能够处理多模态数据分布并加速新城市的适应。此外，针对实际应用中的噪声数据，我们为 MetaStore 设计了一种有效的学习策略，通过为每个训练批次采样高质量数据来促进更快的收敛和优化。广泛的实验结果表明，与各种基线相比，我们提出的方法具有最先进的性能。