Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs
نویسندگان
چکیده
In recent years, online ride-hailing platforms, such as Uber and Didi, have become an indispensable part of urban transportation make our lives more convenient. After a passenger is matched up with driver by the platform, both freedom to simply accept or cancel ride one click. Hence, accurately predicting whether passenger-driver pair good match, i.e., its matching success rate (MSR), turns out be crucial for platforms devise instant strategies order assignment. However, since users consist two parties, decision-making needs simultaneously account dynamics from sides. This makes it challenging than traditional advertising tasks that predict user's response towards object, e.g., click-through prediction advertisements. Moreover, amount available data severely imbalanced across different cities, creating difficulties training accurate model smaller cities scarce data. Though sophisticated neural network architecture can help improve accuracy under scarcity, overly complex design will impede model's capacity delivering timely predictions in production environment. paper, MSR passenger-driver, we propose M ulti- xmlns:xlink="http://www.w3.org/1999/xlink">V iew ( MV ) which comprehensively learns interactions among dynamic features passenger, driver, trip order, well context. Regarding imbalance problem, further xmlns:xlink="http://www.w3.org/1999/xlink">K nowledge xmlns:xlink="http://www.w3.org/1999/xlink">D istillation framework xmlns:xlink="http://www.w3.org/1999/xlink">KD supplement predictive power using knowledge denser data, also generate simple support efficient deployment. Finally, conduct extensive experiments on real-world datasets several demonstrates superiority solution.
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2022
ISSN: ['1558-2191', '1041-4347', '2326-3865']
DOI: https://doi.org/10.1109/tkde.2021.3112739