# TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

arXiv: https://arxiv.org/abs/1812.00073 · HTML: https://mlsanity.com/paper/1812.00073/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 30 of 32)

- [On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models](https://mlsanity.com/paper/2209.05310/) (2022)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence](https://mlsanity.com/paper/2202.12183/) (2022)
- [Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://mlsanity.com/paper/2208.06164/) (2022)
- [LibAUC: A Deep Learning Library for X-Risk Optimization](https://mlsanity.com/paper/2306.03065/) (2023)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022)
- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
- [Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://mlsanity.com/paper/2404.02543/) (2024)
- [ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference](https://mlsanity.com/paper/2204.11458/) (2022)
- [Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems](https://mlsanity.com/paper/2310.10462/) (2023)
- [On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings](https://mlsanity.com/paper/2109.03334/) (2021)
- [CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking](https://mlsanity.com/paper/2112.08766/) (2021)
- ["It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems](https://mlsanity.com/paper/2109.07576/) (2021)
- [Learning to Rank For Push Notifications Using Pairwise Expected Regret](https://mlsanity.com/paper/2201.07681/) (2022)
- [Reusable Self-Attention Recommender Systems in Fashion Industry Applications](https://mlsanity.com/paper/2301.06777/) (2023)
- [Rank4Class: A Ranking Formulation for Multiclass Classification](https://mlsanity.com/paper/2112.09727/) (2021)
- [Reusable Self-Attention-based Recommender System for Fashion](https://mlsanity.com/paper/2211.16366/) (2022)
- [Feature Engineering in Learning-to-Rank for Community Question Answering Task](https://mlsanity.com/paper/2309.07610/) (2023)
- [A Self-boosted Framework for Calibrated Ranking](https://mlsanity.com/paper/2406.08010/) (2024)
- [Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation](https://mlsanity.com/paper/2504.11284/) (2025)
- [LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG](https://mlsanity.com/paper/2601.19535/) (2026)
- [Learning Neural Ranking Models Online from Implicit User Feedback](https://mlsanity.com/paper/2201.06658/) (2022)
- [Federated Unbiased Learning to Rank](https://mlsanity.com/paper/2105.04761/) (2021)
- [Unbiased Pairwise Learning to Rank in Recommender Systems](https://mlsanity.com/paper/2111.12929/) (2021)
- [Learning to Rank Graph-based Application Objects on Heterogeneous Memories](https://mlsanity.com/paper/2211.02195/) (2022)
- [Improving the Security of Smartwatch Payment with Deep Learning](https://mlsanity.com/paper/2307.05437/) (2023)
- [UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems](https://mlsanity.com/paper/2407.09104/) (2024)
- [Weak Supervision for Improved Precision in Search Systems](https://mlsanity.com/paper/2503.07025/) (2025)
- [Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete](https://mlsanity.com/paper/2510.01574/) (2025)

## Abstract (arXiv)

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank.