10. Theme-based PageRank

For many years now, the topic- or theme-based homogeneity of websites has been dicussed as a possible ranking criterion of search engines. There are various theoretical approaches for the implementation of themes in search engine algorithms which all have in common that web pages are no longer ranked only based on their own content, but also based on the content of other

web pages. For example, the content of all pages of one website may take influence on the ranking of one single page of this website. On the other hand, it is also conceivable that one page's ranking is based on the content of those pages which link to it or which it links to itself.

The potential implementation of a theme-based ranking in the Google search engine is discussed controversially. In search engine optimization forums and on websites on this topic we can over and over again find advice that inbound links from sites with a similar theme to our own have a larger influence on PageRank than links from unrelated sites. This hypothesis shall be discussed here. Therefore, we first of all take a look at two relatively new approaches for the integration of themes in the PageRank technique: on the one hand the "intelligent surfer" by Matthew Richardson and Pedro Domingos and on the other hand the Topic-Sensitive PageRank by Taher Haveliwala. Subsequently, we take a look at the possibility of using content analyses in order to compare the text of web pages, which can be a basis for weighting links within the PageRank technique.

The "Intelligent Surfer" by Richardson and Domingos

Matthew Richardson and Pedro Domingos resort to the Random Surfer Model in order to explain their approach for the implementation of themes in the PageRank technique. Instead of a surfer who follws links completely at random, they suggest a more intelligent surfer who, on the one hand, only follows links which are related to an original search query and, on the other hand, also after "getting bored" only jumps to a page which relates to the original query.

So, to Richardson and Domingos' "intelligent surfer" only pages are relevant that contain the search term of an initial query. But since the Random Surfer Model does nothing but illustrate the PageRank technique, the question is how an "intelligent" behaviour of the Random Surfer influences PageRank. The answer is that for every term occuring on the web a separate PageRank calculation has to be conducted and each calculation is solely based on links between pages which contain that term.

Computing PageRank this way causes some problems. They especially appear for search terms that do not occur so often on the web. To make it into the PageRank calculations for a specific search term, that term has not only to appear on someone's page, but also on the pages that link to it. So, the search results would often be based on small subsets of the web and may omit relevant sites. In addition, using such small subsets of the web, the algorithms are more vulnerable to spam by automatically generating numerous pages.

Additionally, there are serious problems regarding scalability. Richardson and Domingos estimate the memory and computing time requirements for several 100,000 terms 100-200 times higher compared to the original PageRank calculations. Regarding the large number of small subsets of the web, these numbers appear to be realistic.

The higher memory requirements should not be so much of a problem because Richardson and Domingos correctly state that the term specific PageRank values constitute only a fraction of the data volume of Google's inverse index. However, the computing time requirements are indeed a large problem. If we assume just five hours for a conventional PageRank calculation, then this would last about 3 weeks based on Richardson and Domingos' model, which makes it unsuitable for actual employment.

10. Theme-Based PageRank (continued)

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© 2002 eFactory Internet-Agentur KG Online-Marketing - written by Markus Sobek
PageRank and Google are trademarks of Google Inc., Mountain ViewCA, USA.
PageRank is protected by US Patent 6,285,999.