Machine learned model for selecting actions for fulfilling order with out-of-stock items

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

• receiving an indication from a picker client device that a requested item from an order to be fulfilled at a source location is unavailable at the source location, wherein the order is associated with a user;
• retrieving model inputs based in part on the indication, wherein the model inputs include availability information for the requested item, at least one other source location, a foundational item status of the requested item, and a tenure of the user, wherein the foundational item status indicates whether the respective requested item is foundational item for the order based on other items in the order;
• selecting a quality improvement action for the requested item based on an output of an order quality model applied to the model inputs, wherein the order quality model is a machine learning model that was trained by:
• accessing a set of training examples including appeasement training data, picker fulfillment training data, user satisfaction training data, and user tenure training data,
• applying the order quality model to the set of training examples to generate a training output corresponding to a predicted quality improvement action,
• back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the order quality model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted quality improvement action, and
• stopping the back-propagation after the one or more loss functions satisfy one or more criteria;
• and
• performing the quality improvement action.

A computer system comprising:

• a processor;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
• receiving an indication from a picker client device that a requested item from an order to be fulfilled at a source location is unavailable at the source location, wherein the order is associated with a user,
• retrieving model inputs based in part on the indication, wherein the model inputs include availability information for the requested item, at least one other source location, a foundational item status of the requested item, and a tenure of the user, wherein the foundational item status indicates whether the respective requested item is foundational item for the order based on other items in the order,
• selecting a quality improvement action for the requested item based on an output of an order quality model applied to the model inputs, wherein the order quality model is a machine learning model that was trained by:
• accessing a set of training examples including appeasement training data, picker fulfillment training data, user satisfaction training data, and user tenure training data,
• applying the order quality model to the set of training examples to generate a training output corresponding to a predicted quality improvement action,
• back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the order quality model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted quality improvement action, and
• stopping the back-propagation after the one or more loss functions satisfy one or more criteria, and
• performing the quality improvement action.