# Enhancing Personalized Ranking With Differentiable Group AUC Optimization

arXiv: https://arxiv.org/abs/2304.09176 · HTML: https://mlsanity.com/paper/2304.09176/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification](https://mlsanity.com/paper/2012.03173/) (2020)
- [Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/1908.10831/) (2019)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [One-Pass AUC Optimization](https://mlsanity.com/paper/1305.1363/) (2013)
- [Stochastic Proximal AUC Maximization](https://mlsanity.com/paper/1906.06053/) (2019)
- [MBA: Mini-Batch AUC Optimization](https://mlsanity.com/paper/1805.11221/) (2018)
- [Confidence-Weighted Bipartite Ranking](https://mlsanity.com/paper/1607.00847/) (2016)

## Built on by (most cited first, 2 of 2)

- [Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems](https://mlsanity.com/paper/2312.12750/) (2023)
- [Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation](https://mlsanity.com/paper/2504.11284/) (2025)

## Abstract (arXiv)

AUC is a common metric for evaluating the performance of a classifier. However, most classifiers are trained with cross entropy, and it does not optimize the AUC metric directly, which leaves a gap between the training and evaluation stage. In this paper, we propose the PDAOM loss, a Personalized and Differentiable AUC Optimization method with Maximum violation, which can be directly applied when training a binary classifier and optimized with gradient-based methods. Specifically, we construct the pairwise exponential loss with difficult pair of positive and negative samples within sub-batches grouped by user ID, aiming to guide the classifier to pay attention to the relation between hard-distinguished pairs of opposite samples from the perspective of independent users. Compared to the origin form of pairwise exponential loss, the proposed PDAOM loss not only improves the AUC and GAUC metrics in the offline evaluation, but also reduces the computation complexity of the training objective. Furthermore, online evaluation of the PDAOM loss on the 'Guess What You Like' feed recommendation application in Meituan manifests 1.40% increase in click count and 0.65% increase in order count compared to the baseline model, which is a significant improvement in this well-developed online life service recommendation system.