  We propose RoBiRank, a ranking algorithm that is motivated by observing a
close connection between evaluation metrics for learning to rank and loss
functions for robust classification. The algorithm shows a very competitive
performance on standard benchmark datasets against other representative
algorithms in the literature. On the other hand, in large scale problems where
explicit feature vectors and scores are not given, our algorithm can be
efficiently parallelized across a large number of machines; for a task that
requires 386,133 x 49,824,519 pairwise interactions between items to be ranked,
our algorithm finds solutions that are of dramatically higher quality than that
can be found by a state-of-the-art competitor algorithm, given the same amount
of wall-clock time for computation.
