Personalised Filter Bias with Google and DuckDuckGo: An Exploratory Study

Author:

Akbar Awais,Caton Simon,Bierig Ralf

Abstract

AbstractPersonalisation in search has improved performance, focus, and user experience to a great extent, however, it also arguably polarises informational perspectives. This paper seeks to illustrate an experimental methodology to quantify how three situational user variables affect personalisation across two search engines: Google and DuckDuckGo. We find that the presence of cookies and prior search history markedly affect the first page of search results on both platforms, but that prior (shallow) browsing history has no observable effect. We also find that there is very little in common between the results of both search engines. We argue that these results advocate more consideration of how personalisation fosters filter biases.

Publisher

Springer Nature Switzerland

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1. Towards Integrating Human-in-the-loop Control in Proactive Intelligent Personalised Agents;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27

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