Toward Understanding Privileged Features Distillation in Learning-to-Rank

In learning-to-rank problems, a \textit{privileged feature} is one that is available during model training, but not available at test time. Such features naturally arise in merchandised recommendation systems; for instance, user clicked this item as a feature is predictive of user purchased this item in the offline data, but is clearly not available during online serving. Another source of privileged features is those that are too expensive to compute online but feasible to be added offline.