Name
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 webmasters. As a matter of fact, search engine optimization (SEO) has became a sizeable business that attempts to improve their clients' ranking. Still, the natural reluctance of search engine companies to reveal their internal mechanisms and the lack of ways to validate SEO's methods have created numerous myths and fallacies associated with ranking algorithms; Google'sin particular. In this paper, we focus on the Google ranking algorithm and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about this popular ranking algorithm. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of reverse engineering Google's ranking algorithm 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 Google's ranking function, provide guidelines for SEOs and webmasters to optimize their web pages, validate or disapprove new ranking features, and evaluate search engine ranking results for possible ranking bias. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WI-IAT.2010.195 | Web Intelligence |
Keywords | Field | DocType |
linear learning models,ranking scheme,learning (artificial intelligence),ranking algorithm,recursive partitioning ranking scheme,ranking function,seo methods,web pages,google ranking algorithm,search engine optimization,google,reverse engineering,new ranking feature,possible ranking bias,google ranking,content-only ranking,internet,ranking feature,search engine ranking evaluation,ranking system,search engines,popular ranking algorithm,search engine evaluation,weed management,recursive partitioning,search engine,learning artificial intelligence | Data mining,Learning to rank,Ranking SVM,Web page,Computer science,Ranking (information retrieval),Artificial intelligence,The Internet,Ranking,Information retrieval,Reverse engineering,Search engine optimization,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-4191-4 | 8 |
PageRank | References | Authors |
0.56 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ao-Jan Su | 1 | 188 | 15.35 |
| Y. Charlie Hu | 2 | 3357 | 181.75 |
| Aleksandar Kuzmanovic | 3 | 960 | 71.99 |
| Cheng-Kok Koh | 4 | 1540 | 134.64 |