# A Self-boosted Framework for Calibrated Ranking

arXiv: https://arxiv.org/abs/2406.08010 · HTML: https://mlsanity.com/paper/2406.08010/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Verified Uncertainty Calibration](https://mlsanity.com/paper/1909.10155/) (2019)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models](https://mlsanity.com/paper/2209.06053/) (2022)
- [TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank](https://mlsanity.com/paper/1812.00073/) (2018)
- [Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://mlsanity.com/paper/2208.06164/) (2022)
- [Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions](https://mlsanity.com/paper/1905.10713/) (2019)
- [Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](https://mlsanity.com/paper/2211.01494/) (2022)
- [Query-dominant User Interest Network for Large-Scale Search Ranking](https://mlsanity.com/paper/2310.06444/) (2023)

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

- [Hierarchical Group-wise Ranking Framework for Recommendation Models](https://mlsanity.com/paper/2506.12756/) (2025)
- [Calibrated Preference Learning: The Case of Label Ranking](https://mlsanity.com/paper/2605.30447/) (2026)

## Abstract (arXiv)

Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously. For instance, in the advertising ranking system, the predicted click-through rate (CTR) is utilized for ranking and required to be calibrated for the downstream cost-per-click ads bidding. Recently, multi-objective based methods have been wildly adopted as a standard approach for Calibrated Ranking, which incorporates the combination of two loss functions: a pointwise loss that focuses on calibrated absolute values and a ranking loss that emphasizes relative orderings. However, when applied to industrial online applications, existing multi-objective CR approaches still suffer from two crucial limitations. First, previous methods need to aggregate the full candidate list within a single mini-batch to compute the ranking loss. Such aggregation strategy violates extensive data shuffling which has long been proven beneficial for preventing overfitting, and thus degrades the training effectiveness. Second, existing multi-objective methods apply the two inherently conflicting loss functions on a single probabilistic prediction, which results in a sub-optimal trade-off between calibration and ranking. To tackle the two limitations, we propose a Self-Boosted framework for Calibrated Ranking (SBCR).