Professor in Political Science and Computer and Information Science

David Lazer

Measuring Personalization of Web Search

Peer Reviewed Computer Science Conference
Publication date: 
05/2013
Authors: 
Ancsa Hannak
Piotr Sapiezynski
Arash Molavi Kakhki
Balachander Krishnamurthy
David Lazer
Alan Mislove
Christo Wilson
Measuring Personalization of Web Search

Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.

In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search; we find that, on average, 11.7% of results show differences due to personalization, but that this varies widely by search query and by result ranking. Third, we investigate the causes of personalization on Google Web Search. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effets of personalization on Web search engines today.

Research Areas TOC

Computational Social Science, 21st Century Democracy, Political Networks

Computational Social Science, Collective Cognition

DNA and the Criminal Justice System

21st Century Democracy, Political Networks