Leveraging Community-Generated Videos and Command Logs to Classify and Recommend Software Workflows
Users of complex software applications often rely on inefficient or suboptimal workflows because they are not aware that better methods exist. In this paper, we develop and validate a hierarchical approach combining topic modeling and frequent pattern mining to classify the workflows offered by an application, based on a corpus of community-generated videos and command logs. We then propose and evaluate a design space of four different workflow recommender algorithms, which can be used to recommend new workflows and their associated videos to software users. An expert validation of the task classification approach found that 82% of the time, experts agreed with the classifications. We also evaluate our workflow recommender algorithms, demonstrating their potential and suggesting avenues for future work.Download publication
See what’s new.
PieCursor: Merging Pointing and Command Selection for Rapid In-place Tool Switching
We describe a new type of graphical user interface widget called the…
Convolutional Neural Networks for Steady Flow Approximation
In aerodynamics related design, analysis and optimization problems,…
A Multi-Scale Stochastic Modelling Primitive for Computer Graphics
Stochastic modelling has been successfully used in computer graphics…
Why is applying Artificial Intelligence in Construction so difficult?
While applying AI in construction can be challenging, Kasia Borowska…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.Contact us