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Collaborating Authors

Deploying CommunityCommands: A Software Command Recommender System Case Study

AAAI Conferences

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a four-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was made available as a publically available plug-in download for Autodesk’s flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 AutoCAD users. In this paper, we present our system usage data and payoff. We also provide an in-depth discussion of the challenges and design issues associated with developing and deploying the front end AutoCAD plug-in and its back end system. This includes a detailed description of the issues surrounding cold start and privacy. We also discuss how our practical system architecture was designed to leverage Autodesk’s existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to end-users. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.


Deploying CommunityCommands: A Software Command Recommender System Case Study

AI Magazine

This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing customer involvement program (CIP) data to deliver in-product contextual recommendations to end users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.


Li

AAAI Conferences

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a four-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was made available as a publically available plug-in download for Autodesk's flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 AutoCAD users.


Deploying CommunityCommands: A Software Command Recommender System Case Study

AI Magazine

This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.


Editorial Introduction to the Special Articles in the Fall Issue

AI Magazine

We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, and citizen science. The 2014 conference continued the tradition with a selection of 7 deployed applications describing systems in use by their intended end users, and 14 emerging applications describing works in progress. This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends.