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End-user feature labeling

TitleEnd-user feature labeling
Publication TypeConference Paper
Year of Publication2011
AuthorsWong, W-K., I. Oberst, S. Das, T. Moore, S. Stumpf, K. McIntosh, and M. M. Burnett
Tertiary AuthorsPu, P., M. Pazzani, E. André, and D. Riecken
Conference NameProceedings of the 15th international conference on Intelligent user interfaces - IUI '11
Pagination115-124
Date Published02/2011
PublisherACM Press
Conference LocationPalo Alto, CA
ISBN Number9781450304191
Abstract

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively.

Notes

Best Paper Nominee

DOI10.1145/1943403.1943423