WMRB: Learning to Rank in a Scalable Batch Training Approach

Kuan Liu; Prem Natarajan

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arXiv · 1711.04015

WMRB: Learning to Rank in a Scalable Batch Training Approach

Kuan Liu·Prem Natarajan

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Abstract

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.

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BibTeX RIS

Kuan Liu, Prem Natarajan. 2017-11-10. WMRB: Learning to Rank in a Scalable Batch Training Approach. https://arxiv.org/abs/1711.04015

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