Machine learning based approach for automatically recommending personalized estimates of time needed to complete a task

A system for automatically recommending personalized estimates of amounts of time needed to complete a task, the system comprising:

• one or more processors;
• and
• one or more memory configured to store computer executable instructions that, when executed by the one or more processors, cause the one or more processors to:
• provide a plurality time-to-complete (TTC) models trained at different quantile levels to a machine learning model configured to select one TTC model of the plurality of TTC models as a selected TTC model to generate an estimated amount of time needed for a current user to complete the task, wherein each TTC model of the plurality of TTC models comprises a quantile regression model trained using historical data that includes an actual amount of time taken by different users to complete the task;
• obtain the estimated amount of time from the selected TTC model;
• obtain feedback data regarding the estimated amount of time obtained from the selected TTC model, wherein the feedback data indicates whether the current user completed the task;
• and
• train the machine learning model based, at least in part, on the feedback data, wherein to train the machine learning model the instructions cause the one or more processors to:
• in response to the feedback data indicating the current user completed the task, increment a confidence variable associated with the machine learning model;
• or
• in response to the feedback data indicating the current user did not complete the task, decrement the confidence variable associated with the machine learning model;
• and
• display, on a display of a user device, the estimated amount of time obtained from the selected TTC model.

A non-transitory computer-readable medium comprising instructions to be executed in a computer system to automatically recommending personalized estimates of amounts of time needed to complete a task, wherein the instructions when executed in the computer system cause the computer system to:

• provide a plurality time-to-complete (TTC) models trained at different quantile levels to a machine learning model configured to select one TTC model of the plurality of TTC models as a selected TTC model to generate an estimated amount of time needed for a current user to complete the task, wherein each TTC model of the plurality of TTC models comprises a quantile regression model trained using historical data that includes an actual amount of time taken by different users to complete the task;
• obtain the estimated amount of time from the selected TTC model;
• obtain feedback data regarding the estimated amount of time obtained from the selected TTC model, wherein the feedback data indicates whether the current user completed the task;
• and
• train the machine learning model based, at least in part, on the feedback data, wherein to train the machine learning model the instructions cause the computing system to:
• in response to the feedback data indicating the current user completed the task, increment a confidence variable associated with the machine learning model;
• or
• in response to the feedback data indicating the current user did not complete the task, decrement the confidence variable associated with the machine learning model;
• and
• display, on a display of a user device, the estimated amount of time obtained from the selected TTC model.