Improving Machine Learning Beyond the Algorithm
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In interactive machine learning systems (IMLSs), such as search engines, social networks, and e-commerce sites, machine learning algorithms and user interfaces are inseparably linked. My thesis demonstrates that improving the accuracy of the machine learning algorithm in such systems is not only a question of the algorithm itself, but also a question of the user interface that directly affects the properties of feedback data the machine learner receives. To this end, this thesis introduces the concept of feedback-enhancing interface design as an alternate and complementary pathway to better machine learning performance. As I will show in this thesis, feedback-enhancing interfaces allow us to effectively shape the quantity as well as the quality of the obtained feedback data, all while maintaining usability and user experience.
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2018-08-30
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machine learning; Human-Computer Interaction; artifical intelligence; interactive systems; Computer science
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Joachims, Thorsten
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Frazier, Peter
Kleinberg, Robert David
Bennett, Paul
Kleinberg, Robert David
Bennett, Paul
Degree Discipline
Computer Science
Degree Name
Ph. D., Computer Science
Degree Level
Doctor of Philosophy
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dissertation or thesis