Search (2 results, page 1 of 1)

Mowshowitz, A.; Kawaguchi, A.: Assessing bias in search engines (2002) 0.00
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Abstract

This paper deals with the measurement of bias in search engines on the World Wide Web. Bias is taken to mean the balance and representativeness of items in a collection retrieved from a database for a set of queries. This calls for assessing the degree to which the distribution of items in a collection deviates from the ideal. Ascertaining this ideal poses problems similar to those associated with determining relevance in the measurement of recall and precision. Instead of enlisting subject experts or users to determine such an ideal, a family of comparable search engines is used to approximate it for a set of queries. The distribution is obtained by computing the frequencies of occurrence of the uniform resource locators (URLs) in the collection retrieved by several search engines for the given queries. Bias is assessed by measuring the deviation from the ideal of the distribution produced by a particular search engine.
Mowshowitz, A.; Kawaguchi, A.: Measuring search engine bias (2005) 0.00
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Abstract

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.