MindMiner: Quantifying Entity Similarity via Interactive Distance Metric Learning
Pages 93 - 96
Abstract
We present MindMiner, a mixed-initiative interface for capturing subjective similarity measurements via a combination of new interaction techniques and machine learning algorithms. MindMiner collects qualitative, hard to express similarity measurements from users via active polling with uncertainty and example based visual constraint creation. MindMiner also formulates human prior knowledge into a set of inequalities and learns a quantitative similarity distance metric via convex optimization. In a 12-participant peer-review understanding task, we found MindMiner was easy to learn and use, and could capture users' implicit knowledge about writing performance and cluster target entities into groups that match subjects' mental models.
Supplementary Material
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- MindMiner: Quantifying Entity Similarity via Interactive Distance Metric Learning
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Published In
March 2015
164 pages
ISBN:9781450333085
DOI:10.1145/2732158
- General Chairs:
- Oliver Brdiczka,
- Polo Chau,
- Program Chairs:
- Giuseppe Carenini,
- Shimei Pan,
- Per Ola Kristensson
Copyright © 2015 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 March 2015
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- LRDC RDF
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IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces
March 29 - April 1, 2015
Georgia, Atlanta, USA
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IUI '15 Companion Paper Acceptance Rate 47 of 205 submissions, 23%;
Overall Acceptance Rate 746 of 2,811 submissions, 27%
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