Year: 2011
Type: preprint
Abstract: The many metrics employed for the evaluation of search engine results have not themselves been conclusively evaluated. We propose a new measure for a metric's ability to identify user preference of result lists. Using this measure, we evaluate the metrics Discounted Cumulated Gain, Mean Average Precision and classical precision, finding that the former performs best. We also show that considering more results for a given query can impair rather than improve a metric's ability to predict user preferences.
Source: arXiv (Cornell University)
Author: Sirotkin, Pavel
Institution: Heinrich Heine University Düsseldorf
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W196206262