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.
%0 Conference Paper
%1 citeulike:13971601
%A Wiebe, Michelle
%A Geiskkovitch, Denise Y.
%A Bunt, Andrea
%B Proceedings of the 21st International Conference on Intelligent User Interfaces
%C New York, NY, USA
%D 2016
%I ACM
%K adaptive-interface recommender
%P 43--47
%R 10.1145/2856767.2856814
%T Exploring User Attitudes Towards Different Approaches to Command Recommendation in Feature-Rich Software
%U http://dx.doi.org/10.1145/2856767.2856814
%X 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.
%@ 978-1-4503-4137-0
@inproceedings{citeulike:13971601,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Wiebe, Michelle and Geiskkovitch, Denise Y. and Bunt, Andrea},
biburl = {https://www.bibsonomy.org/bibtex/2afaf99ad4d05e1251664d2feb799e61b/aho},
booktitle = {Proceedings of the 21st International Conference on Intelligent User Interfaces},
citeulike-article-id = {13971601},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2463656.2856814},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/2856767.2856814},
doi = {10.1145/2856767.2856814},
interhash = {a1200a131a724b3ccb569182889fa678},
intrahash = {afaf99ad4d05e1251664d2feb799e61b},
isbn = {978-1-4503-4137-0},
keywords = {adaptive-interface recommender},
location = {Sonoma, California, USA},
pages = {43--47},
posted-at = {2016-03-07 19:42:27},
priority = {3},
publisher = {ACM},
series = {IUI '16},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Exploring User Attitudes Towards Different Approaches to Command Recommendation in Feature-Rich Software}},
url = {http://dx.doi.org/10.1145/2856767.2856814},
year = 2016
}