The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Search Engine Marketing, Inc.: Driving Search Traffic to Your Company's Web Site
Search Engine Marketing, Inc.: Driving Search Traffic to Your Company's Web Site
Identifying link farm spam pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
PLANET: massively parallel learning of tree ensembles with MapReduce
Proceedings of the VLDB Endowment
How to Improve Your Google Ranking: Myths and Reality
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Communications of the ACM
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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.