> While most learning-to-rank methods learn the ranking function by minimizing the loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking function. In this work, we reveal the relationship between ranking measures and loss functions in learning-to-rank methods, such as Ranking SVM, RankBoost, RankNet, and ListMLE.
> we have proved that many pairwise/listwise losses in learning to rank are actually upper bounds of measure-based ranking errors. As a result, the minimization of these loss functions will lead to the maximization of the ranking measures. The key to obtaining this result is to model ranking as a sequence of classification tasks, and define a so-called essential loss as the weighted sum of the classification errors of individual tasks in the sequence.
> We have also shown a way to improve existing methods
by introducing appropriate weights to their loss functions.