# Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

arXiv: https://arxiv.org/abs/2310.10462 · HTML: https://mlsanity.com/paper/2310.10462/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 67 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 30 of 30)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://mlsanity.com/paper/2010.05874/) (2020)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Differentiable Ranks and Sorting using Optimal Transport](https://mlsanity.com/paper/1905.11885/) (2019)
- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https://mlsanity.com/paper/1903.08850/) (2019)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft](https://mlsanity.com/paper/2106.14876/) (2021)
- [Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task](https://mlsanity.com/paper/2111.02086/) (2021)
- [MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks](https://mlsanity.com/paper/2203.06801/) (2022)
- [TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank](https://mlsanity.com/paper/1812.00073/) (2018)
- [Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision](https://mlsanity.com/paper/2105.04019/) (2021)
- [PiRank: Scalable Learning To Rank via Differentiable Sorting](https://mlsanity.com/paper/2012.06731/) (2020)
- [Monotonic Differentiable Sorting Networks](https://mlsanity.com/paper/2203.09630/) (2022)
- [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://mlsanity.com/paper/2212.10218/) (2022)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [Rank and Rate: Multi-task Learning for Recommender Systems](https://mlsanity.com/paper/1807.11698/) (2018)
- [MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration](https://mlsanity.com/paper/2202.04348/) (2022)
- [GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation](https://mlsanity.com/paper/2207.14467/) (2022)
- [DCAF: A Dynamic Computation Allocation Framework for Online Serving System](https://mlsanity.com/paper/2006.09684/) (2020)
- [Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking](https://mlsanity.com/paper/2307.11096/) (2023)
- [Cooperative Retriever and Ranker in Deep Recommenders](https://mlsanity.com/paper/2206.14649/) (2022)
- [HLT-MT: High-resource Language-specific Training for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2207.04906/) (2022)
- [Deep Multi-view Learning to Rank](https://mlsanity.com/paper/1801.10402/) (2018)

## Built on by (most cited first, 6 of 6)

- [C-ICL: Contrastive In-context Learning for Information Extraction](https://mlsanity.com/paper/2402.11254/) (2024)
- [Scaling Laws for Online Advertisement Retrieval](https://mlsanity.com/paper/2411.13322/) (2024)
- [GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models](https://mlsanity.com/paper/2507.06507/) (2025)
- [Differentiable Fast Top-K Selection for Large-Scale Recommendation](https://mlsanity.com/paper/2510.11472/) (2025)
- [Learning Cascade Ranking as One Network](https://mlsanity.com/paper/2503.09492/) (2025)
- [Adaptive2: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling](https://mlsanity.com/paper/2412.08198/) (2024)

## Abstract (arXiv)

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.