论文标题
产品分配空间需求的概率模拟器
A Probabilistic Simulator of Spatial Demand for Product Allocation
论文作者
论文摘要
在在线和离线贸易中，将消费者与相关产品联系在一起是一个非常重要的问题。在物理零售中，产品放置是将消费者与产品联系起来的有效方法。但是，在商店内选择产品位置可能是一个乏味的过程。此外，由于数据的稀缺以及物理世界中的探索和实验高昂的成本，在离线零售业中学习重要的空间模式是具有挑战性的。为了应对这些挑战，我们提出了一种物理零售中空间需求的随机模型。我们表明，所提出的模型比现有基线更能预测需求。我们还对不同的自动化技术进行了初步研究，并表明可以通过深入学习可以学习最佳产品分配策略。
Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a store can be a tedious process. Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world. To address these challenges, we propose a stochastic model of spatial demand in physical retail. We show that the proposed model is more predictive of demand than existing baselines. We also perform a preliminary study into different automation techniques and show that an optimal product allocation policy can be learned through Deep Q-Learning.
