Tabs.do: Task-Centric Browser Tab Management
Joseph Chee Chang, Yongsung Kim, Victor Miller, Michael Xieyang Liu, Brad A. Myers, Aniket Kittur
Abstract
Despite the increasing complexity and scale of people’s online activities, browser interfaces have stayed largely the same since tabs were introduced in major browsers nearly 20 years ago. The gap between simple tab-based browser interfaces and the complexity of users’ tasks can lead to serious adverse effects – commonly referred to as “tab overload.” This paper introduces a Chrome extension called Tabs.do, which explores bringing a task-centric approach to the browser, helping users to group their tabs into tasks and then organize, prioritize, and switch between those tasks fluidly. To lower the cost of importing, Tabs.do uses machine learning to make intelligent suggestions for grouping users’ open tabs into task bundles by exploiting behavioral and semantic features. We conducted a field deployment study where participants used Tabs.do with their real-life tasks in the wild, and showed that Tabs.do can decrease tab clutter, enabled users to create rich task structures with lightweight interactions, and allowed participants to context-switch among tasks more efficiently.
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Cited by top-tier papers10
- Wigglite: Low-cost Information Collection and TriageMichael Xieyang Liu, Andrew Kuznetsov, Yongsung Kim, Joseph Chee Chang et al.UIST 2022 · 62 citations
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- Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language ModelsMichael Xieyang Liu, Tongshuang Wu, Tianying Chen, Franklin Mingzhe Li et al.CHI 2024 · 44 citations
- Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision MakingMichael Xieyang Liu, Aniket Kittur, Brad A. MyersCHI 2022 · 31 citations
- Fuse: In-Situ Sensemaking Support in the BrowserAndrew Kuznetsov, Joseph Chee Chang, Nathan Hahn, Napol Rachatasumrit et al.UIST 2022 · 29 citations
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