A Probabilistic Simulator of Spatial Demand for Product Allocation
  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.
