How a Search Engine might Adjust Rankings based upon Patterns in Query and Click Logs

Imagine that a number of people use Google to perform a search for “orange,” and then “banana,” and then “pineapple” and then choose the web page “” in the search results they see.

Now imagine that Google looks at the information it collects about what people do when they search, and finds in its query logs and click logs that there are a large number, a statistically significant number, of people who search for “orange,” and then “banana,” and then “pineapple,” or possibly the same search terms in a slightly different order, and then tend to click on “”

Google may also notice that there are people looking for some very related terms during query sessions, such as consecutive searches for “banana,” “apple” and “pineapple.”

Since this second set of queries for “banana,” “apple” and “pineapple,” is so similar to the query sessions that contained the search terms “orange” and “banana” and “pineapple,” where people were choosing the page “,” Google may choose to adjust the ranking for “,” for people using those very related terms in their search sessions.

Google was granted a patent on this process this past week:

Rank-adjusted content items
Invented by Mayur Datar, Kedar Dhamdhere, and Ashutosh Garg
Assigned to Google
US Patent 7,610,282
Granted October 27, 2009
Filed March 30, 2007


Click logs and query logs are processed to identify statistical search patterns. A search session is compared to the statistical search patterns. Content items responsive to a query of the search session are identified, and a ranking of the content items is adjusted based on the comparison.


Author: Bill Slawski

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