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Design and Evaluation of a Command Recommendation System for Software Applications

ACM Transactions on Computer-Human Interaction

Design and Evaluation of a Command Recommendation System for Software Applications (1:50 min.)

Video title (x:xx min.)


We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. An online evaluation showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.

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Software Learning

The Learning project aims to investigate advanced techniques for assisting users in learning complicated applications. We are interested in a range of investigations from the scientific study of the human learning process to prototyping novel interaction techniques for improving the general learning mechanisms that can be applied to all applications.


Amazon recommends books to its users. Netflix recommends movies. With CommunityCommands, Autodesk will recommend command functionality to its users. CommunityCommands collects usage data from thousands of Autodesk users, through the Customer Involvement Program (CIP), and then generates personalized command recommendations using newly developed algorithms. CommunityCommands will expose users to the critical commands which they should be using, but are not aware of, accelerating the learning process.


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