# Multi-Label Learning to Rank through Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2207.03060 · HTML: https://mlsanity.com/paper/2207.03060/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates](https://mlsanity.com/paper/1905.09997/) (2019)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [On Application of Learning to Rank for E-Commerce Search](https://mlsanity.com/paper/1903.04263/) (2019)
- [An Alternative Cross Entropy Loss for Learning-to-Rank](https://mlsanity.com/paper/1911.09798/) (2019)
- [Multi-objective Ranking via Constrained Optimization](https://mlsanity.com/paper/2002.05753/) (2020)

## Built on by (most cited first, 10 of 10)

- [Federated Multi-Objective Learning](https://mlsanity.com/paper/2310.09866/) (2023)
- [COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework](https://mlsanity.com/paper/2410.08316/) (2024)
- [Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow](https://mlsanity.com/paper/2311.13159/) (2023)
- [Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems](https://mlsanity.com/paper/2406.17475/) (2024)
- [Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation](https://mlsanity.com/paper/2504.11284/) (2025)
- [No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods](https://mlsanity.com/paper/2412.12092/) (2024)
- [Predicting Potential Customer Support Needs and Optimizing Search Ranking in a Two-Sided Marketplace](https://mlsanity.com/paper/2503.17329/) (2025)
- [STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning](https://mlsanity.com/paper/2506.19883/) (2025)
- [Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments](https://mlsanity.com/paper/2602.23234/) (2026)
- [A Momentum-Based Variance-Reduced Algorithm for Federated Multiobjective Optimization](https://mlsanity.com/paper/2608.22945/) (2026)

## Abstract (arXiv)

Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measurements make the ground truth ranking non-unique with respect to a single relevance criterion. To resolve ambiguity, it is desirable to train a model using many relevance criteria, giving rise to Multi-Label LTR (MLLTR). Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e.g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue. In this research, we leverage the Multi-Objective Optimization (MOO) aspect of the MLLTR problem and employ recently developed MOO algorithms to solve it. Specifically, we propose a general framework where the information from labels can be combined in a variety of ways to meaningfully characterize the trade-off among the goals. Our framework allows for any gradient based MOO algorithm to be used for solving the MLLTR problem. We test the proposed framework on two publicly available LTR datasets and one e-commerce dataset to show its efficacy.