MO-LightGBM: A Library for Multi-objective Learning to Rank with LightGBM
Chaosheng Dong, Michinari Momma · 2025
This paper introduces MO-LightGBM, an open-source library built upon LightGBM, specifically designed to offer an integrated, versatile, and easily adaptable framework for Multi-objective Learning to Rank (MOLTR). MO-LightGBM supports diverse Multi-objective optimization (MOO) settings and incorporates 12 state-of-the-art optimization strategies. Its modular architecture enhances usability and flexibility, allowing researchers and practitioners to easily develop new MOO methodologies, perform rigorous comparisons with existing techniques, and effectively deploy MOO algorithms in practical ranking applications. We illustrate the utility of MO-LightGBM through a Bi-objective Learning to Rank example and present visualizations of the results. MO-LightGBM is available at https://github.com/amazon-science/MO-LightGBM.