Machine learning prediction of picker accepting a new order for fulfillment before completing existing batch of orders

A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:

• receiving, at an online concierge system, information describing a progress of a picker servicing a set of existing orders;
• predicting a first likelihood that the picker will finish servicing the set of existing orders within a threshold amount of time, wherein predicting the first likelihood is based at least in part on the progress of the picker and information describing the set of existing orders;
• determining whether the first likelihood exceeds a threshold likelihood;
• responsive to determining the first likelihood exceeds the threshold likelihood, accessing a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders for servicing while servicing the set of existing orders, wherein the machine learning model is trained by:
• receiving historical data describing acceptance, by pickers, of requests to service sets of new orders while servicing sets of existing orders, and
• training the machine learning model based at least in part on the historical data;
• applying the machine learning model to a set of inputs to predict the second likelihood that the picker will accept the set of new orders for servicing while servicing the set of existing orders, wherein the set of inputs comprises a set of attributes of the picker and the progress of the picker;
• matching a plurality of sets of new orders with a plurality of pickers based at least in part on the second likelihood;
• determining whether one or more sets of new orders are matched with the picker;
• and
• responsive to determining the one or more sets of new orders are matched with the picker, sending one or more requests to service the one or more sets of new orders to a client device associated with the picker.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
• receiving, at an online concierge system, information describing a progress of a picker servicing a set of existing orders;
• predicting a first likelihood that the picker will finish servicing the set of existing orders within a threshold amount of time, wherein predicting the first likelihood is based at least in part on the progress of the picker and information describing the set of existing orders;
• determining whether the first likelihood exceeds a threshold likelihood;
• responsive to determining the first likelihood exceeds the threshold likelihood, accessing a machine learning model trained to predict a second likelihood that the picker will accept a set of new orders for servicing while servicing the set of existing orders, wherein the machine learning model is trained by:
• receiving historical data describing acceptance, by pickers, of requests to service sets of new orders while servicing sets of existing orders, and
• training the machine learning model based at least in part on the historical data;
• applying the machine learning model to a set of inputs to predict the second likelihood that the picker will accept the set of new orders for servicing while servicing the set of existing orders, wherein the set of inputs comprises a set of attributes of the picker and the progress of the picker;
• matching a plurality of sets of new orders with a plurality of pickers based at least in part on the second likelihood;
• determining whether one or more sets of new orders are matched with the picker;
• and
• responsive to determining the one or more sets of new orders are matched with the picker, sending one or more requests to service the one or more sets of new orders to a client device associated with the picker.