Graph-Structure-Based Grouping of Web Browser Tabs into Tasks
by Mizuki Hisatomi, Keishi Tajima
Abstract
Web browser tabs allow users to work on multiple tasks in
parallel. However, when tabs from different tasks are mixed, it
becomes difficult to locate the desired tab and maintain
focus. Grouping tabs by task can enhance usability, but manual
grouping imposes a high cognitive burden, and existing machine
learning-based approaches typically require costly manual annotations
and rely on privacy-sensitive data such as URLs. In this paper, we
propose a method that automatically groups tabs by task without
requiring manual annotation or access to sensitive data. Our method
builds a directed graph based on tab-switching history and applies a
community detection algorithm to identify task-related groups. To
evaluate our approach, we conducted a comparative experiment using
data collected from eight participants. We compared our method against
a variant of an existing machine learning-based approach, modified to
operate without private data. Experimental results demonstrate that
our method achieves comparable or superior performance without the
need for manual annotation.
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