Techniques for classifying and recommending software workflows

Abstract

In various embodiments, a pattern-based recommendation subsystem automatically recommends workflows for software-based tasks. In operation, the pattern-based recommendation subsystem computes an expected distribution of frequencies across command patterns based on different distributions of frequencies across the command patterns. The expected distribution of frequencies is associated with a target user, and each different distribution of frequencies is associated with a different user. The pattern-based recommendation subsystem then applies a 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. Subsequently, the pattern-based recommendation subsystem determines a training item based on the expected distribution of frequencies and the target distribution of weights. The pattern-based recommendation subsystem generates a recommendation that specifies the training item. Finally, the pattern-based recommendation subsystem transmits the recommendation to a user to assist the user in performing a particular task.