Techniques for classifying and recommending software workflows

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to automatically recommend workflows for software-based tasks by performing the steps of:

• computing an expected distribution of frequencies across a set of command patterns based on different distributions of frequencies across the set of command patterns, wherein the expected distribution of frequencies is associated with a target user, and each different distribution of frequencies is associated with a different user;
• applying a first set of commands associated with the target user to a trained machine-learning model to determine a target distribution of weights applied to a set of tasks, wherein the trained machine-learning model maps different sets of commands to different distributions of weights applied to the set of tasks;
• determining a first training item from a plurality of training items based on the expected distribution of frequencies and the target distribution of weights;
• generating a recommendation that specifies the first training item;
• and
• transmitting the recommendation to a user to assist the user in performing a particular task.

A system, comprising:

• one or more memories storing instructions;
• and
• one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
• compute an expected distribution of frequencies across a set of command patterns based on different distributions of frequencies across the set of command patterns, wherein the expected distribution of frequencies is associated with a target user, and each different distribution of frequencies is associated with a different user;
• apply a first set of commands associated with the target user to a trained machine-learning model to determine a target distribution of weights applied to a set of tasks, wherein the trained machine-learning model maps different sets of commands to different distributions of weights applied to the set of tasks;
• determine a first training item from a plurality of training items based on the expected distribution of frequencies and the target distribution of weights;
• generate a recommendation that specifies the first training item;
• and
• transmit the recommendation to a user to assist the user in performing a particular task.