There has been speculation in the search engine optimization community about the potential implementation of Topic-Sensitive PageRank by Google. Topic-Sensitive PageRank (TSPR) is considered by some to be a further refinement of Google’s well known PageRank system.
The idea of a TSPR originated with Taher H. Haveliwala of the Stanford University Department of Computer Science in 2002. In a paper entitled Topic-Sensitive Page-Rank, the concept was first introduced at the Proceedings of the Eleventh International World Wide Web Conference, in 2002. The paper, in its entirety, can be found here.
Keep in mind that the paper offered at the conference was one method of implementation only. The actual calculation used might be quite different. What is important is the overall concept of TSPR, and its implications for the search engine optimization community.
How is Topic-Sensitive PageRank different from PageRank?
To fully understand the implications of Topic-Sensitive PageRank, we need to first briefly examine the current Google PageRank (PR) system.
PageRank (spelled as one word) is a Google trademarked technology. It was designed as a numerical system of ranking the relative importance of web pages, and created at Stanford University in California by Google founders Larry Page and Sergey Brin.
The concept they used was, in Google's own words, to calculate the "uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value."
If Google's definition is taken literally, the entire system rests on the incoming and outgoing links from the billions of web pages that form the Internet. On the surface, the system seems simple enough. If web page A links to web page B, Google considers web page A as actually voting for the importance of web page B.
While the system of PageRank is far more complicated than that simple definition, it serves our purposes for this article.
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