  List-wise based learning to rank methods are generally supposed to have
better performance than point- and pair-wise based. However, in real-world
applications, state-of-the-art systems are not from list-wise based camp. In
this paper, we propose a new non-linear algorithm in the list-wise based
framework called ListMLE, which uses the Plackett-Luce (PL) loss. Our
experiments are conducted on the two largest publicly available real-world
datasets, Yahoo challenge 2010 and Microsoft 30K. This is the first time in the
single model level for a list-wise based system to match or overpass
state-of-the-art systems in real-world datasets.
