  We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch
loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss
(WARP). WMRB uses a new rank estimator and an efficient batch training
algorithm. The approach allows more accurate item rank approximation and
explicit utilization of parallel computation to accelerate training. In three
item recommendation tasks, WMRB consistently outperforms WARP and other
baselines. Moreover, WMRB shows clear time efficiency advantages as data scale
increases.
