Exploring User Attitudes Towards Different Approaches to Command Recommendation in Feature-Rich Software

Feature-rich software applications offer users hundreds of commands, yet most people use only a very small fraction of the available command set. Command recommenders aim to increase awareness of an application’s capabilities by generating personalized recommendations for new commands. A primary distinguishing characteristic of these recommenders concerns the manner in which they determine command relevance. Social approaches do so by analyzing community usage logs, whereas, task-based approaches mine web documentation for logical command clusters. Through a qualitative study with sixteen participants, in this work we explored user attitudes towards these different approaches and the supplemental information they enable. Author Keywords Software learnability; Recommender systems; Explanations ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

• Type: Journal
• Year: 2016
• Where: IUI
• Authors: Michelle Wiebe, Denise Y. Geiskkovitch, Andrea Bunt
• Keywords: IUI 2016, Software Engineering