DeepMTL2R: A Library for Deep Multi-task Learning to Rank
This provides a scalable solution for modern ranking systems with multiple objectives, but it is incremental as it builds on existing multi-task learning and transformer methods.
The paper tackles the problem of optimizing multiple relevance criteria simultaneously in ranking systems by introducing DeepMTL2R, an open-source deep learning framework that integrates heterogeneous signals using transformer self-attention, and reports competitive performance on a public dataset.
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}.