1. A method, comprising: computing a click relevance ranking of a first pair and a second pair based upon user log data, the first pair comprising a query and a first URL, the second pair comprising the query and a second URL, the computing comprising: reducing click bias associated with at least one of the first pair or the second pair by determining a normalized click rate from the user log data; creating, after the reducing, a directed acyclic graph of a relevance relationship between the first pair and the second pair, the directed acyclic graph comprising a representation of at least one of the first URL or the second URL, the representation in the directed acyclic graph directly connected in the directed acyclic graph to a second representation of one or more URLs in the directed acyclic graph; and mapping the directed acyclic graph into a linear ordering to ascertain the click relevance ranking.
1. A method, comprising: computing a click relevance ranking of a first pair and a second pair based upon user log data, the first pair comprising a query and a first URL, the second pair comprising the query and a second URL, the computing comprising: reducing click bias associated with at least one of the first pair or the second pair by determining a normalized click rate from the user log data; creating, after the reducing, a directed acyclic graph of a relevance relationship between the first pair and the second pair, the directed acyclic graph comprising a representation of at least one of the first URL or the second URL, the representation in the directed acyclic graph directly connected in the directed acyclic graph to a second representation of one or more URLs in the directed acyclic graph; and mapping the directed acyclic graph into a linear ordering to ascertain the click relevance ranking. 5. The method of claim 1 , comprising mapping the directed acyclic graph into the linear ordering using a flooding technique.