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How to Improve Your Search Engine Ranking: Myths and Reality

Published:01 March 2014Publication History
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Abstract

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’ ranking. Still, the lack of ways to validate SEO’s methods has created numerous myths and fallacies associated with ranking algorithms.

In this article, we focus on two ranking algorithms, Google’s and Bing’s, 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’s 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.

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    • Published in

      cover image ACM Transactions on the Web
      ACM Transactions on the Web  Volume 8, Issue 2
      March 2014
      226 pages
      ISSN:1559-1131
      EISSN:1559-114X
      DOI:10.1145/2600093
      Issue’s Table of Contents

      Copyright © 2014 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 March 2014
      • Accepted: 1 October 2013
      • Revised: 1 September 2013
      • Received: 1 February 2011
      Published in tweb Volume 8, Issue 2

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