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Web"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>Search engines have greatly influenced the way people access information on the Internet, as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked Web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for Web masters. As a matter of fact, Search Engine Optimization (SEO) has became a sizeable business that attempts to improve their clients\u2019 ranking. Still, the lack of ways to validate SEO\u2019s methods has created numerous myths and fallacies associated with ranking algorithms.<\/jats:p>\n          <jats:p>In this article, we focus on two ranking algorithms, Google\u2019s and Bing\u2019s, and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about these popular ranking algorithms. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of predicting ranking results with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78% of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10 ones for 77% of search terms. We show how our ranking system can be used to reveal the relative importance of ranking features in a search engine\u2019s ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results for possible ranking bias.<\/jats:p>","DOI":"10.1145\/2579990","type":"journal-article","created":{"date-parts":[[2014,4,1]],"date-time":"2014-04-01T13:06:54Z","timestamp":1396357614000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["How to Improve Your Search Engine Ranking"],"prefix":"10.1145","volume":"8","author":[{"given":"Ao-Jan","family":"Su","sequence":"first","affiliation":[{"name":"Northwestern University"}]},{"given":"Y. Charlie","family":"Hu","sequence":"additional","affiliation":[{"name":"Purdue University"}]},{"given":"Aleksandar","family":"Kuzmanovic","sequence":"additional","affiliation":[{"name":"Northwestern University"}]},{"given":"Cheng-Kok","family":"Koh","sequence":"additional","affiliation":[{"name":"Purdue University"}]}],"member":"320","published-online":{"date-parts":[[2014,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"AccuraCast. 2007. Google algorithm\u2019s top ranking factors. http:\/\/www.accuracast.com\/seo-weekly\/ranking-factors.php.  AccuraCast. 2007. Google algorithm\u2019s top ranking factors. http:\/\/www.accuracast.com\/seo-weekly\/ranking-factors.php."},{"key":"e_1_2_1_2_1","unstructured":"Alexa. 2014. Alexa the web information company. http:\/\/www.alexa.com\/.  Alexa. 2014. Alexa the web information company. http:\/\/www.alexa.com\/."},{"key":"e_1_2_1_3_1","unstructured":"Anderson S. 2007. Google seo test google prefers valid HTML and CSS. http:\/\/www.hobo-web.co.uk\/seo-blog\/index.php\/official-google-prefers-valid-html-css\/.  Anderson S. 2007. Google seo test google prefers valid HTML and CSS. http:\/\/www.hobo-web.co.uk\/seo-blog\/index.php\/official-google-prefers-valid-html-css\/."},{"key":"e_1_2_1_4_1","unstructured":"Aubuchon V. 2010. Google ranking factors. http:\/\/www.vaughns-1-pagers.com\/internet\/google-ranking-factors.htm.  Aubuchon V. 2010. Google ranking factors. http:\/\/www.vaughns-1-pagers.com\/internet\/google-ranking-factors.htm."},{"volume-title":"Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb\u201905)","author":"Benczur A. A.","key":"e_1_2_1_5_1"},{"key":"e_1_2_1_6_1","unstructured":"Blog1. 2013. How google works: Why does crappy website rank higher than mine? http:\/\/www.trafficgenerationcafe.com\/how-google-works-relevance\/.  Blog1. 2013. 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RSYNC version 3 alpha out - O\u2019reilly onlamp blog. http:\/\/www.oreillynet.com\/onlamp\/blog\/2007\/10\/rsync_version_3_alpha_out.html.  Gift N. 2007. RSYNC version 3 alpha out - O\u2019reilly onlamp blog. http:\/\/www.oreillynet.com\/onlamp\/blog\/2007\/10\/rsync_version_3_alpha_out.html."},{"key":"e_1_2_1_16_1","unstructured":"Google. 2014a. Pagerank on Google toolbar. http:\/\/www.google.com\/support\/toolbar\/bin\/answer.py?hl=en&answer=79837.  Google. 2014a. Pagerank on Google toolbar. http:\/\/www.google.com\/support\/toolbar\/bin\/answer.py?hl=en&answer=79837."},{"key":"e_1_2_1_17_1","unstructured":"Google. 2014b. Google trends. http:\/\/www.google.com\/trends.  Google. 2014b. Google trends. http:\/\/www.google.com\/trends."},{"key":"e_1_2_1_18_1","unstructured":"Google. 2014c. Google webmaster tools. http:\/\/www.google.com\/webmasters\/.  Google. 2014c. 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