论文标题
通过约束优化的多目标排名
Multi-objective Ranking via Constrained Optimization
论文作者
论文摘要
在本文中，我们引入了一种增强的基于拉格朗日的方法，以将多个目标（MO）纳入搜索排名算法中。优化MOS是在生产中建立排名模型的基本和现实的要求。所提出的方法在约束优化中制定了MO，并在流行的增强框架中解决了问题，这是我们工作的新贡献。此外，我们提出了一个程序，以设置问题中的所有优化参数。实验结果表明，该方法比现有方法成功地达到了MO标准的效率要高得多。
In this paper, we introduce an Augmented Lagrangian based method to incorporate the multiple objectives (MO) in a search ranking algorithm. Optimizing MOs is an essential and realistic requirement for building ranking models in production. The proposed method formulates MO in constrained optimization and solves the problem in the popular Boosting framework -- a novel contribution of our work. Furthermore, we propose a procedure to set up all optimization parameters in the problem. The experimental results show that the method successfully achieves MO criteria much more efficiently than existing methods.
