Trained computer model for identification of wrong delivery location for an order placed at an online system

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, via a user interface of an online system, an input from a user of the online system, the input including a delivery location for an order;
• comparing the received delivery location with a delivery location associated with the user stored in a database of the online system;
• responsive to identifying that the received delivery location and the stored delivery location represent different delivery locations, accessing a machine-learning model of the online system, wherein the machine-learning model is trained to predict a likelihood of the received delivery location being correct, the machine-learning model trained by:
• generating training data that include labels about whether delivery locations for orders placed at the online system by users of the online system were accurate, the labels generated based on responses by the users about accuracies of the delivery locations, and
• generating, using the training data, a set of initial values for a set of parameters of the machine-learning model;
• receiving, via a network from a device associated with the user, user data collected by sensors on the device, the user data including information about a location of the device when the user placed the order;
• applying the machine-learning model to one or more features of the order and the information about the location of the device to predict the likelihood of the received delivery location being correct;
• generating, based on the predicted likelihood, a confidence score of the received delivery location being correct;
• responsive to the confidence score being below a threshold score, generating a user interface of the device displaying a confirmation screen with one or more alternative delivery locations prompting the user to verify accuracy of the received delivery location;
• receiving, from the device and via the network, a response by the user to the confirmation screen that corrects an error in relation to the delivery location and was recorded at the device;
• replacing, based on the response by the user, the delivery location in the database with an updated delivery location of the one or more alternative delivery locations;
• and
• re-training the machine-learning model by updating, using the received response, the set of parameters of the machine-learning model.

A computer system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising:
• receiving, via a user interface of an online system, an input from a user of the online system, the input including a delivery location for an order;
• comparing the received delivery location with a delivery location associated with the user stored in a database of the online system;
• responsive to identifying that the received delivery location and the stored delivery location represent different delivery locations, accessing a machine-learning model of the online system, wherein the machine-learning model is trained to predict a likelihood of the received delivery location being correct, the machine-learning model trained by:
• generating training data that include labels about whether delivery locations for orders placed at the online system by users of the online system were accurate, the labels generated based on responses by the users about accuracies of the delivery locations, and
• generating, using the training data, a set of initial values for a set of parameters of the machine-learning model;
• receiving, via a network from a device associated with the user, user data collected by sensors on the device, the user data including information about a location of the device when the user placed the order;
• applying the machine-learning model to one or more features of the order and the information about the location of the device to predict the likelihood of the received delivery location being correct;
• generating, based on the predicted likelihood, a confidence score of the received delivery location being correct;
• responsive to the confidence score being below a threshold score, generating a user interface of the device displaying a confirmation screen with one or more alternative delivery locations prompting the user to verify accuracy of the received delivery location;
• receiving, from the device and via the network, a response by the user to the confirmation screen that corrects an error in relation to the delivery location and was recorded at the device;
• replacing, based on the response by the user, the delivery location in the database with an updated delivery location of the one or more alternative delivery locations;
• and
• re-training the machine-learning model by updating, using the received response, the set of parameters of the machine-learning model.