# WMRB: Learning to Rank in a Scalable Batch Training Approach

arXiv: https://arxiv.org/abs/1711.04015 · HTML: https://mlsanity.com/paper/1711.04015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Learning to Rank For Push Notifications Using Pairwise Expected Regret](https://mlsanity.com/paper/2201.07681/) (2022)

## Abstract (arXiv)

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.