Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing
<|reference_start|>Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing: In the mobile internet era, the Online Food Ordering Service (OFOS) emerges as an integral component of inclusive finance owing to the convenience it brings to people. OFOS platforms offer dynamic allocation incentives to users and merchants through diverse marketing campaigns to encourage payments while maintaining the platforms' budget efficiency. Despite significant progress, the marketing domain continues to face two primary challenges: (i) how to allocate a limited budget with greater efficiency, demanding precision in predicting users' monotonic response (i.e. sensitivity) to incentives, and (ii) ensuring spatio-temporal adaptability and robustness in diverse marketing campaigns across different times and locations. To address these issues, we propose a Constrained Monotonic Adaptive Network (CoMAN) method for spatio-temporal perception within marketing pricing. Specifically, we capture spatio-temporal preferences within attribute features through two foundational spatio-temporal perception modules. To further enhance catching the user sensitivity differentials to incentives across varied times and locations, we design modules for learning spatio-temporal convexity and concavity as well as for expressing sensitivity functions. CoMAN can achieve a more efficient allocation of incentive investments during pricing, thus increasing the conversion rate and orders while maintaining budget efficiency. Extensive offline and online experimental results within our diverse marketing campaigns demonstrate the effectiveness of the proposed approach while outperforming the monotonic state-of-the-art method.<|reference_end|>
@article{li2024enhancing,
    title={Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in
  Diverse Marketing},
    author={Bin Li, Jiayan Pei, Feiyang Xiao, Yifan Zhao, Zhixing Zhang, Diwei
  Liu, HengXu He, Jia Jia},
    journal={arXiv preprint arXiv:2406.14132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14132},
    primaryClass={cs.AI}
}