Abstract
Vehicle-cargo matching is a key task in freight O2O platform, which involves the complex interactions of drivers, vehicles, cargos, cargo owners and environmental context. Many existing works mainly study the matching of vehicle routing problems, the matching based on the credit evaluation of both drivers and cargo owners, and the matching based on game theory from the perspective of management. Since the freight O2O platform is also the producer of big data, this study proposes a driver CTR prediction model for vehicle-cargo matching task from the perspective of data mining. Specifically, we first use the bottom-level attention network to model fine-grained preferences in driver historical behaviors, such as route interest and search interest, as well as fine-grained preferences such as vehicle type and vehicle length interest, and route interest in cargo owner historical behaviors. Then, the driver basic profile vector, cargo owner basic profile vector, cargo description vector, driver preferences vector and cargo owner preferences vector are feeded into the neural network composed of two deep components for feature interactions learning, and then a top-level attention network is used to learn the influencing factors of different information on the vehicle-cargo matching task. Finally, a multi-classifier is used for matching prediction. We conduct comprehensive experiments on real dataset, and the results show that, compared with the existing solutions, considering user preferences and adopting deep components collaborative modeling can improve the performance of vehicle-cargo matching to a certain extent, which verifies the effectiveness and superiority of the proposed model.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (NO.72261003, 62276196, 62106070), Guizhou Provincial Science and Technology Project (NO.Qiankehejichu-ZK[2022]yiban019), and Key Research and Development Project of Hubei Province (NO.2021BAA030).
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Jiang, Z., Zuo, X., Yuan, K., Li, L., Wang, D., Tao, X. (2023). HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_45
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DOI: https://doi.org/10.1007/978-3-031-46674-8_45
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