Readings in information retrieval
Readings in information retrieval
Journal of the American Society for Information Science
Finding information on the World Wide Web: the retrieval effectiveness of search engines
Information Processing and Management: an International Journal
Architecture of a metasearch engine that supports user information needs
Proceedings of the eighth international conference on Information and knowledge management
Assessing bias in search engines
Information Processing and Management: an International Journal
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
A study of results overlap and uniqueness among major web search engines
Information Processing and Management: an International Journal
Proceedings of the 3rd International Semantic Search Workshop
Web search solved?: all result rankings the same?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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This paper examines a real-time measure of bias in Web search engines. The measure captures the degree to which the distribution of URLs, retrieved in response to a query, deviates from an ideal or fair distribution for that query. This ideal is approximated by the distribution produced by a collection of search engines. Differences between bias and classical retrieval measures are highlighted by examining the possibilities for bias in four extreme cases of recall and precision. The results of experiments examining the influence on bias measurement of subject domains, search engines, and search terms are presented. Three general conclusions are drawn: (1) the performance of search engines can be distinguished with the aid of the bias measure; (2) bias values depend on the subject matter under consideration; (3) choice of search terms does not account for much of the variance in bias values. These conclusions underscore the need to develop "bias profiles" for search engines.