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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.


Deploying CommunityCommands: A Software Command Recommender System Case Study

AI Magazine

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 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. CommunityCommands was a publically available plug-in for Autodesk’s flagship software application AutoCAD. 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. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.


Practical Recommender Systems - Programmer Books

#artificialintelligence

Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.


Introduction to Recommender Systems

@machinelearnbot

Netflix values the recommendation engine powering its content suggestions at $1 billion per year and Amazon says its system drives a 20-35% lift in sales annually. What makes these systems so valuable? The answer lies in the data science that powers them.


Recommender Systems

Communications of the ACM

The use of recommender systems has exploded over the last decade, making personalized recommendations ubiquitous online. Most of the major companies, including Google, Facebook, Twitter, LinkedIn, Netflix, Amazon, Microsoft, Yahoo!, eBay, Pandora, Spotify, and many others use recommender systems (RS) within their services. These systems are used to recommend a whole range of items, including consumer products, movies, songs, friends, news articles, restaurants and various others. Recommender systems constitute a mission-critical technology in several companies. For example, Netflix reports that at least 75% of its downloads and rentals come from their RS, thus making it of strategic importance to the company.a