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TrustRank is a technique proposed by Zoltán Gyöngyi and Hector Garcia-Molina of Stanford University and Jan Pedersen of Yahoo! to semi-automatically separate useful webpages from spam.
Many Web spam pages are only created with the intention of misleading search engines. These pages, chiefly created for commercial reasons, use various techniques to achieve higher-than-deserved rankings on the search engines' result pages. While human experts can easily identify spam, it is too expensive to manually evaluate a large number of pages.
One popular method for improving rankings is to artificially increase the perceived importance of a document through complex linking schemes. Google's PageRank and similar methods for determining the relative importance of Web documents have been subjected to manipulation.
The TrustRank method calls for selecting a small set of seed pages to be evaluated by an expert. Once the reputable seed pages are manually identified, a crawl extending outward from the seed set seeks out similarly reliable and trustworthy pages. TrustRank's reliability diminishes as documents become further removed from the seed set.
The researchers who proposed the TrustRank methodology have continued to refine their work by evaluating related topics, such as measuring spam mass.
See also
External links
- Combating Web Spam with TrustRank
- TrustRank algorithm
- Link-based spam detection Yahoo! assigned patent application using Trustrank
"TrustRank"