« Back to Projects list
Personalized Command Recommendations based on Web Documentation (2016)
Prior work on command recommendations for feature-rich software has relied on data supplied by a large community of users to generate personalized recommendations. In this work, we explore the feasibility of using an alternative data source: web documentation. Specifically, our approach uses QF-Graphs, a previously proposed technique that maps higher-level tasks (i.e., search queries) to commands referenced in online documentation. Our approach uses these command-to-task mappings as an automatically generated plan library, enabling our prototype system to make personalized recommendations for task-relevant commands. Through both offline and online evaluations, we explore potential benefits and drawbacks of this approach.
Exploring User Attitudes Towards Different Approaches to Command Recommendation
Michelle Wiebe, Denise Y. Geiskkovitch, and Andrea Bunt. "Exploring User Attitudes Towards Different Approaches to Command Recommendation in Feature-Rich Software." Proceedings of the 21st International Conference on Intelligent User Interfaces - IUI'16
Exploring Personalized Command Recommendations based on Information Found in Web Documentation
Khan, M. A. A., Dziubak, V., & Bunt, A. (2015, March). Exploring personalized command recommendations based on information found in Web documentation. In Proceedings of the 20th International Conference on Intelligent User Interfaces (pp. 225-235). ACM.
QFRecs - Recommending Features in Feature-Rich Software based on Web Documentation
Md Adnan Alam Khan. QFRecs - Recommending Features in Feature-Rich Software based on Web Documentation. M.Sc. Thesis (2015). University of Manitoba, Canada.