It is often of interest to learn a context-sensitive decision policy, such as in contextual multi-armed bandit processes. To quantify the efficiency of a machine learning algorithm for such settings, probably approximately correct (PAC) bounds, which bound the number of samples required, or cumulative regret guarantees, are typically used. However, real-world settings often have limited resources for experimentation, and decisions/interventions may differ in the amount of resources required (e.g., money or time). Therefore, it is of interest to consider how to design an experiment strategy that reduces the experimental budget needed to learn a near-optimal contextual policy. Unlike reinforcement learning or bandit approaches that embed costs into the reward function, we focus on reducing resource use in learning a near-optimal policy without resource constraints. We introduce two resource-aware algorithms for the contextual bandit setting and prove their soundness. Simulations based on real-world datasets demonstrate that our algorithms significantly reduce the resources needed to learn a near-optimal decision policy compared to previous resource-unaware methods.

Small and micro merchants in the traditional FastMoving Consumer Goods (FMCG) retail business play a vital role in developing economies. In Indonesia, 82% of the population gets their day-to-day groceries from traditional trade merchants. Our work aims to help these grocery merchants prosper by increasing their sales and improving their inventory turnover cycle. To this end, we conduct an in-depth analysis of merchants’ inventory-buying behaviors from a transaction record dataset and propose several item recommendation methods for merchants’ inventory based on insights gathered from the data. For analysis, we investigate the stock-keeping unit (SKU) coverage, the SKU selling speed, merchant repeat purchase patterns, the growth rate of monthly spending, as well as their correlations with total monetary spending by merchants. Our recommendation methods consider recency and utility of SKU selling speed, and are implemented using popular item-based recommendation and content-based filtering algorithms. The inventory recommendation system was deployed and tested in production. Initial results show a lift in click-through rate (CTR) and also show that merchants are accepting the recommendations of SKU items for their inventory that they had never bought before.