The idea behind Pairwise Learning to Rank is that if you have a set of search results then a clicked on result can be used as training example to indicate that it should rank more highly that the results above it which were not clicked on.
Let's say you have a search engine, which you collect a lot of click data for. Now you tweak the algorithm and run it against your training set to see if it's an improvement to the algorithm. We know which examples it does better on because it places some clicked items above non-clicked items. How do we know where it is doing worse?