Suppose that you have clickthrough data in the following form -- (query, clicked url, frequency). I wonder if there is any way of using the data to train a ranking function. Naively, you can treat each click as a ground-truth url and frequency as some kind of weight -- that is, frequently clicked url is treated as a more important training instance than a less frequent one. But, it seems too naive and I wonder if there is any other better method.
I am aware of the famous paper by Thorsten Joachims, 'Optimizing search engines using clickthrough data'. The paper however assumes a different form of clickthrough data -- (query, a list of documents ranked by search system, a set of clicked URLs). So, I cannot directly apply his method to my clickthrough data.