# Learning to Rank For Push Notifications Using Pairwise Expected Regret

arXiv: https://arxiv.org/abs/2201.07681 · HTML: https://mlsanity.com/paper/2201.07681/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank](https://mlsanity.com/paper/1812.00073/) (2018)
- [Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology](https://mlsanity.com/paper/1905.12767/) (2019)
- [Related Pins at Pinterest: The Evolution of a Real-World Recommender System](https://mlsanity.com/paper/1702.07969/) (2017)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests](https://mlsanity.com/paper/2003.00359/) (2020)
- [WMRB: Learning to Rank in a Scalable Batch Training Approach](https://mlsanity.com/paper/1711.04015/) (2017)

## Built on by (most cited first, 3 of 3)

- [Should I send this notification? Optimizing push notifications decision making by modeling the future](https://mlsanity.com/paper/2202.08812/) (2022)
- [AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks](https://mlsanity.com/paper/2501.05809/) (2025)
- [A Self-Triggered Agentic Push Recommendation System](https://mlsanity.com/paper/2608.01949/) (2026)

## Abstract (arXiv)

Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push notifications and discuss the unique challenges this presents compared to traditional ranking problems. To address these challenges, we introduce a novel ranking loss based on weighting the pairwise loss between candidates by the expected regret incurred for misordering the pair. We demonstrate that the proposed method can outperform prior methods both in a simulated environment and in a production experiment on a major social network.