Multi-objective Ranking via Constrained Optimization
This addresses the practical need for efficient multi-objective ranking in production systems, though it appears incremental as it builds on existing Boosting frameworks.
The paper tackles the problem of optimizing multiple objectives in search ranking algorithms by introducing an Augmented Lagrangian method that formulates multi-objective optimization as constrained optimization within a Boosting framework, achieving significantly more efficient performance than existing methods.
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.