一个支持多目标排序的开源深度学习框架，可统一优化多种相关性指标。
DeepMTL2R: A Library for Deep Multi-task Learning to Rank
- 基于Transformer自注意力机制融合多种相关性信号，实现上下文感知建模。
- 集成21种前沿多任务学习算法，支持寻找帕累托最优排序模型。
- 适用于需要平衡多个排序目标的研究与工业场景，如推荐系统。
本文提出DeepMTL2R，一个开源的深度学习框架，用于多任务学习排序（MTL2R），即需同时优化多个相关性标准。该框架利用Transformer的自注意力机制，将异构的相关性信号整合到统一的上下文感知模型中，从而有效处理多样且可能冲突的目标。框架内置21种前沿多任务学习算法，支持多目标优化以识别帕累托最优排序模型。通过捕捉项目与标签间的复杂依赖关系和长距离交互，DeepMTL2R为现代排序系统提供可扩展、高表达力的解决方案，并支持对不同MTL策略的可控对比。我们在公开数据集上验证了其有效性，报告了具有竞争力的性能，并可视化了各目标间的权衡。DeepMTL2R开源地址：https://github.com/amazon-science/DeepMTL2R。
原文摘要 · Abstract (English)
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{https://github.com/amazon-science/DeepMTL2R}{https://github.com/amazon-science/DeepMTL2R}.
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