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.

我们提出了一种约束性的单调自适应网络（CoMAN）方法，用于空间和时间感知的市场定价，以实现更高效的激励投资分配，从而提高转化率和订单量，同时保持预算效率。