  In recent years, online ride-hailing platforms have become an indispensable
part of urban transportation. After a passenger is matched up with a driver by
the platform, both the passenger and the driver have the freedom to simply
accept or cancel a ride with one click. Hence, accurately predicting whether a
passenger-driver pair is a good match turns out to be crucial for ride-hailing
platforms to devise instant order assignments. However, since the users of
ride-hailing platforms consist of two parties, decision-making needs to
simultaneously account for the dynamics from both the driver and the passenger
sides. This makes it more challenging than traditional online advertising
tasks. Moreover, the amount of available data is severely imbalanced across
different cities, creating difficulties for training an accurate model for
smaller cities with scarce data. Though a sophisticated neural network
architecture can help improve the prediction accuracy under data scarcity, the
overly complex design will impede the model's capacity of delivering timely
predictions in a production environment. In the paper, to accurately predict
the MSR of passenger-driver, we propose the Multi-View model (MV) which
comprehensively learns the interactions among the dynamic features of the
passenger, driver, trip order, as well as context. Regarding the data imbalance
problem, we further design the Knowledge Distillation framework (KD) to
supplement the model's predictive power for smaller cities using the knowledge
from cities with denser data and also generate a simple model to support
efficient deployment. Finally, we conduct extensive experiments on real-world
datasets from several different cities, which demonstrates the superiority of
our solution.
