Machine learning model for predicting future availability of items to evaluate potential replacement items during order fulfillment

Abstract

An online concierge system receives orders and allocates orders to pickers who obtain items in an order from a retailer and deliver the items to a customer from whom the order was received. When an item included in an order is unavailable, the online concierge system suggests one or more replacement items for the item. To select a replacement item for an unavailable item, the online concierge system generates replacement scores for each of a set of candidate replacement items. A replacement score for a candidate replacement item is generated from a probability of the customer performing a positive action when the unavailable item is replaced by the candidate replacement item and a predicted probability of a picker finding the candidate replacement item at the retailer. Based on replacement scores for various candidate replacement items for an unavailable item, the online concierge system selects one or more candidate replacement items.