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User-context for adaptive user interfaces

Published: 28 January 2007 Publication History

Abstract

We present results from an empirical user-study with ten users which investigates if information from a user's environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subject's desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophant's performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.

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cover image ACM Conferences
IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces
January 2007
388 pages
ISBN:1595934812
DOI:10.1145/1216295
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 January 2007

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Author Tags

  1. context
  2. learning classifier systems
  3. machine learning
  4. user-context

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  • (2024)The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video MeetingsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661507(1638-1657)Online publication date: 1-Jul-2024
  • (2024)WorkFit: Designing Proactive Voice Assistance for the Health and Well-Being of Knowledge WorkersProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665561(1-14)Online publication date: 8-Jul-2024
  • (2024)CoExplorer: Generative AI Powered 2D and 3D Adaptive Interfaces to Support Intentionality in Video MeetingsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650797(1-10)Online publication date: 11-May-2024
  • (2023)The Placebo Effect of Artificial Intelligence in Human–Computer InteractionACM Transactions on Computer-Human Interaction10.1145/352922529:6(1-32)Online publication date: 11-Jan-2023
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