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[2602.14519] DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Summary
DeepMTL2R is an open-source library designed for deep multi-task learning to rank, integrating diverse relevance signals into a unified model using transformer architectures.
Why It Matters
This library addresses the challenge of optimizing multiple relevance criteria simultaneously in ranking systems, which is crucial for improving the performance of information retrieval and recommendation systems. By leveraging advanced techniques like self-attention, it enhances the capability to manage complex dependencies and trade-offs among objectives.
Key Takeaways
- DeepMTL2R integrates multiple relevance signals for improved ranking.
- The library supports 21 state-of-the-art multi-task learning algorithms.
- Utilizes transformer architectures to capture complex dependencies.
- Enables controlled comparisons across various MTL strategies.
- Demonstrates competitive performance on publicly available datasets.
Computer Science > Machine Learning arXiv:2602.14519 (cs) [Submitted on 16 Feb 2026] Title:DeepMTL2R: A Library for Deep Multi-task Learning to Rank Authors:Chaosheng Dong, Peiyao Xiao, Yijia Wang, Kaiyi Ji View a PDF of the paper titled DeepMTL2R: A Library for Deep Multi-task Learning to Rank, by Chaosheng Dong and 3 other authors View PDF HTML (experimental) Abstract:This paper presents DeepMTL2R, an open-source deep learning framework for Multi-task Learning to Rank (MTL2R), where multiple relevance criteria must be optimized simultaneously. DeepMTL2R integrates heterogeneous relevance signals into a unified, context-aware model by leveraging the self-attention mechanism of transformer architectures, enabling effective learning across diverse and potentially conflicting objectives. The framework includes 21 state-of-the-art multi-task learning algorithms and supports multi-objective optimization to identify Pareto-optimal ranking models. By capturing complex dependencies and long-range interactions among items and labels, DeepMTL2R provides a scalable and expressive solution for modern ranking systems and facilitates controlled comparisons across MTL strategies. We demonstrate its effectiveness on a publicly available dataset, report competitive performance, and visualize the resulting trade-offs among objectives. DeepMTL2R is available at \href{this https URL}{this https URL}. Subjects: Machine Learning (cs.LG); Information Retrieval (cs.IR) Cite as: arXiv:2602.14519 ...
