A peek at Google's search quality team

From Slashdot, a good article on Amit Singhal and the Google search quality team (run by Udi Manber, formerly of A9). The article talks about the "200 signals" that the ranking algorithm considers when deciding what rank to give pages in response to a search.

The signals are not just the data from the page, but also metadata like the frequency of changes for a page and personalization information like previous searches you've run (if you're logged in, of course!)

The interesting thing is the description of how they evaluate queries. Each query is run against a number of classifiers that decide what kind of search it is and therefore what kind of pages to return for the search. This is probably one of those instances where having the logs of billions upon billions of searches and the computing power to analyze them gives Google a distinct advantage.

Also, keep in mind that all of this computation is done in far less than a second!

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This is Stephen Green's blog. It's about the theory and practice of text search engines, with occasional forays into recommendation and other technologies that can use a good text search engine. Steve is the PI of the Information Retrieval and Machine Learning project in Oracle Labs.

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