A technique for measuring the relative size and overlap of public Web search engines
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Analysis of a very large web search engine query log
ACM SIGIR Forum
Scholarly use of Internet-based electronic resources
Journal of the American Society for Information Science and Technology
Measuring Search Engine Quality
Information Retrieval
U.S. versus European web searching trends
ACM SIGIR Forum
SIAM Journal on Discrete Mathematics
New measurements for search engine evaluation proposed and tested
Information Processing and Management: an International Journal
A coherent measurement of web-search relevance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A similarity measure for indefinite rankings
ACM Transactions on Information Systems (TOIS)
Toward approximate GML retrieval based on structural and semantic characteristics
ICWE'10 Proceedings of the 10th international conference on Web engineering
Google, bing and a new perspective on ranking similarity
Proceedings of the 20th ACM international conference on Information and knowledge management
Measuring the Importance of Users in a Social Network Based on Email Communication Patterns
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Web mining based extraction of problem solution ideas
Expert Systems with Applications: An International Journal
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The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results. We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall's W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.