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

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Abstract

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.

Citation

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

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