Evaluating search engines and defining a consensus implementation

TL;DR

This paper proposes a method to compare search engines by defining a consensus relevance based on pages' visibility across multiple engines, enabling analysis of biases and ranking differences.

Contribution

It introduces a formal model for consensus relevance and a metric to evaluate and compare search engine rankings systematically.

Findings

The model quantifies search engine bias towards own content.

The consensus search engine shows the most visible results across multiple engines.

Analysis reveals differences in relevance and bias among search engines.

Abstract

Different search engines provide different outputs for the same keyword. This may be due to different definitions of relevance, and/or to different knowledge/anticipation of users' preferences, but rankings are also suspected to be biased towards own content, which may prejudicial to other content providers. In this paper, we make some initial steps toward a rigorous comparison and analysis of search engines, by proposing a definition for a consensual relevance of a page with respect to a keyword, from a set of search engines. More specifically, we look at the results of several search engines for a sample of keywords, and define for each keyword the visibility of a page based on its ranking over all search engines. This allows to define a score of the search engine for a keyword, and then its average score over all keywords. Based on the pages visibility, we can also define the consensus search engine as the one showing the most visible results for each keyword. We have implemented this model and present an analysis of the results.