Optimising tab management with machine learning to improve user experience

Manuel Muñoz Martinez; Lynsay A. Shepherd

doi:10.1007/978-3-031-93412-4_24

Optimising tab management with machine learning to improve user experience

Manuel Muñoz Martinez, Lynsay A. Shepherd^*

^*Corresponding author for this work

Department of Cybersecurity and Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Tabbed browsing can often become a counter-intuitive experience. After opening dozens of tabs, the user interface becomes cluttered, which can lead to a decrease in productivity, unnecessary cognitive load, and a detrimental user experience. This paper presents an exploratory web browser extension capable of organising tabs automatically, grouping them by the use of both K-means and DBSCAN clustering. A preliminary user survey (n = 40) was conducted to gain insights regarding browsing behaviours and assess the need for tab organisation. The Google Chrome extension developed employed a Python backend to apply the clustering methods to the tabs. After comparing the performance of both approaches through a final user survey (n = 41), no significant differences were found between preferences for the different grouping mechanisms. The work demonstrates how machine learning algorithms can be leveraged within the browser to create a tab management tool, with the potential to enhance the user experience.

Original language	English
Title of host publication	Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings
Subtitle of host publication	6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I
Editors	Helmut Degen, Stavroula Ntoa
Place of Publication	Cham
Publisher	Springer
Pages	396-410
Number of pages	15
Volume	15819
ISBN (Electronic)	9783031934124
ISBN (Print)	9783031934117
DOIs	https://doi.org/10.1007/978-3-031-93412-4_24
Publication status	Published - 1 Jun 2025
Event	HCI International 2025: 27th International Conference on Human-Computer Interaction - Gothia Towers Hotel and Swedish Exhibition & Congress Centre, Gothenburg, Sweden Duration: 22 Jun 2025 → 27 Jun 2025 Conference number: 27th https://2025.hci.international/

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Publisher	Springer
Volume	15819
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	HCI International 2025: 27th International Conference on Human-Computer Interaction
Abbreviated title	HCII 2025
Country/Territory	Sweden
City	Gothenburg
Period	22/06/25 → 27/06/25
Internet address	https://2025.hci.international/

Keywords

Tab management
Tab grouping
User experience
Machine learning
Browser extension

Access to Document

10.1007/978-3-031-93412-4_24

Embargoed Document

Shepherd_OptimisingTabManagement_Accepted_2025
Accepted author manuscript, 766 KB
Embargo ends: 30/05/26

Cite this

Martinez, M. M., & Shepherd, L. A. (2025). Optimising tab management with machine learning to improve user experience. In H. Degen, & S. Ntoa (Eds.), Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings: 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I (Vol. 15819, pp. 396-410). (Lecture Notes in Computer Science (LNCS); Vol. 15819). Springer. https://doi.org/10.1007/978-3-031-93412-4_24

Martinez, Manuel Muñoz ; Shepherd, Lynsay A. / Optimising tab management with machine learning to improve user experience. Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings: 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I. editor / Helmut Degen ; Stavroula Ntoa. Vol. 15819 Cham : Springer, 2025. pp. 396-410 (Lecture Notes in Computer Science (LNCS)).

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title = "Optimising tab management with machine learning to improve user experience",

abstract = "Tabbed browsing can often become a counter-intuitive experience. After opening dozens of tabs, the user interface becomes cluttered, which can lead to a decrease in productivity, unnecessary cognitive load, and a detrimental user experience. This paper presents an exploratory web browser extension capable of organising tabs automatically, grouping them by the use of both K-means and DBSCAN clustering. A preliminary user survey (n = 40) was conducted to gain insights regarding browsing behaviours and assess the need for tab organisation. The Google Chrome extension developed employed a Python backend to apply the clustering methods to the tabs. After comparing the performance of both approaches through a final user survey (n = 41), no significant differences were found between preferences for the different grouping mechanisms. The work demonstrates how machine learning algorithms can be leveraged within the browser to create a tab management tool, with the potential to enhance the user experience.",

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note = "{\textcopyright} 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG Data availability statement: Not present.; HCI International 2025: 27th International Conference on Human-Computer Interaction, HCII 2025 ; Conference date: 22-06-2025 Through 27-06-2025",

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Martinez, MM & Shepherd, LA 2025, Optimising tab management with machine learning to improve user experience. in H Degen & S Ntoa (eds), Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings: 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I. vol. 15819, Lecture Notes in Computer Science (LNCS), vol. 15819, Springer, Cham, pp. 396-410, HCI International 2025: 27th International Conference on Human-Computer Interaction, Gothenburg, Sweden, 22/06/25. https://doi.org/10.1007/978-3-031-93412-4_24

Optimising tab management with machine learning to improve user experience. / Martinez, Manuel Muñoz; Shepherd, Lynsay A.
Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings: 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I. ed. / Helmut Degen; Stavroula Ntoa. Vol. 15819 Cham: Springer, 2025. p. 396-410 (Lecture Notes in Computer Science (LNCS); Vol. 15819).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimising tab management with machine learning to improve user experience

AU - Martinez, Manuel Muñoz

AU - Shepherd, Lynsay A.

N1 - Conference code: 27th

PY - 2025/6/1

Y1 - 2025/6/1

N2 - Tabbed browsing can often become a counter-intuitive experience. After opening dozens of tabs, the user interface becomes cluttered, which can lead to a decrease in productivity, unnecessary cognitive load, and a detrimental user experience. This paper presents an exploratory web browser extension capable of organising tabs automatically, grouping them by the use of both K-means and DBSCAN clustering. A preliminary user survey (n = 40) was conducted to gain insights regarding browsing behaviours and assess the need for tab organisation. The Google Chrome extension developed employed a Python backend to apply the clustering methods to the tabs. After comparing the performance of both approaches through a final user survey (n = 41), no significant differences were found between preferences for the different grouping mechanisms. The work demonstrates how machine learning algorithms can be leveraged within the browser to create a tab management tool, with the potential to enhance the user experience.

AB - Tabbed browsing can often become a counter-intuitive experience. After opening dozens of tabs, the user interface becomes cluttered, which can lead to a decrease in productivity, unnecessary cognitive load, and a detrimental user experience. This paper presents an exploratory web browser extension capable of organising tabs automatically, grouping them by the use of both K-means and DBSCAN clustering. A preliminary user survey (n = 40) was conducted to gain insights regarding browsing behaviours and assess the need for tab organisation. The Google Chrome extension developed employed a Python backend to apply the clustering methods to the tabs. After comparing the performance of both approaches through a final user survey (n = 41), no significant differences were found between preferences for the different grouping mechanisms. The work demonstrates how machine learning algorithms can be leveraged within the browser to create a tab management tool, with the potential to enhance the user experience.

U2 - 10.1007/978-3-031-93412-4_24

DO - 10.1007/978-3-031-93412-4_24

M3 - Conference contribution

SN - 9783031934117

VL - 15819

T3 - Lecture Notes in Computer Science (LNCS)

SP - 396

EP - 410

BT - Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings

A2 - Degen, Helmut

A2 - Ntoa, Stavroula

PB - Springer

CY - Cham

T2 - HCI International 2025: 27th International Conference on Human-Computer Interaction

Y2 - 22 June 2025 through 27 June 2025

ER -

Martinez MM, Shepherd LA. Optimising tab management with machine learning to improve user experience. In Degen H, Ntoa S, editors, Artificial Intelligence in HCI - 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings: 6th International Conference, AI-HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part I. Vol. 15819. Cham: Springer. 2025. p. 396-410. (Lecture Notes in Computer Science (LNCS)). Epub 2025 May 30. doi: 10.1007/978-3-031-93412-4_24

Optimising tab management with machine learning to improve user experience

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Keywords

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