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Erin Fitzhenry

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Publications by Erin Fitzhenry (bibliography)

» 2009 «

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Shen, Jianqiang, Fitzhenry, Erin and Dietterich, Thomas G. (2009): Discovering frequent work procedures from resource connections. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 277-286. Available online

“Intelligent desktop assistants could provide more help for users if they could learn models of the users' workflows. However, discovering desktop workflows is difficult because they unfold over extended periods of time (days or weeks) and they are interleaved with many other workflows because of user multi-tasking. This paper describes an approach to discovering desktop workflows based on rich instrumentation of information flow actions such as copy/paste, SaveAs, file copy, attach file to email message, and save attachment. These actions allow us to construct a graph whose nodes are files, email messages, and web pages and whose edges are these information flow actions. A class of workflows that we call work procedures can be discovered by applying graph mining algorithms to find frequent subgraphs. This paper describes an algorithm for mining frequent closed connected subgraphs and then describes the results of applying this method to data collected from a group of real users.”

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» 2008 «

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Stumpf, Simone, Sullivan, Erin, Fitzhenry, Erin, Oberst, Ian, Wong, Weng-Keen and Burnett, Margaret (2008): Integrating rich user feedback into intelligent user interfaces. In: Proceedings of the 2008 International Conference on Intelligent User Interfaces 2008. pp. 50-59. Available online

“The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user's knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions.”

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