Cost-Sensitive Learning to Rank

  • Ryan McBride Simon Fraser University
  • Ke Wang Simon Fraser University
  • Zhouyang Ren Chongqing University
  • Wenyuan Li Chongqing University


We formulate the Cost-Sensitive Learning to Rank problem of learning to prioritize limited resources to mitigate the most costly outcomes. We develop improved ranking models to solve this problem, as verified by experiments in diverse domains such as forest fire prevention, crime prevention, and preventing storm caused outages in electrical networks.

AAAI Technical Track: Machine Learning