面向产品分配的空间需求概率模拟器
A Probabilistic Simulator of Spatial Demand for Product Allocation
摘要：
将消费者与相关产品联系起来是在线和离线商务中非常重要的问题。在实体零售中，植入式广告是将消费者与产品联系起来的有效方式。然而，在商店内选择产品位置可能是一个繁琐的过程。此外，由于数据的稀缺性以及物理世界中探索和实验的高成本，学习线下零售中的重要空间模式具有挑战性。为了应对这些挑战，我们提出了一个随机模型的空间需求在物理零售。我们表明，该模型比现有的基线更能预测需求。我们还对不同的自动化技术进行了初步研究，并表明可以通过深度Q学习来学习最佳产品分配策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
