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。

提升多样化营销中的单调建模：借助时空自适应感知

Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in  Diverse Marketing

Large Language Models (LLMs) can adapt to new tasks via in-context learning
(ICL). ICL is efficient as it does not require any parameter updates to the
trained LLM, but only few annotated examples as input for the LLM. In this
work, we investigate an active learning approach for ICL, where there is a
limited budget for annotating examples. We propose a model-adaptive
optimization-free algorithm, termed AdaICL, which identifies examples that the
model is uncertain about, and performs semantic diversity-based example
selection. Diversity-based sampling improves overall effectiveness, while
uncertainty sampling improves budget efficiency and helps the LLM learn new
information. Moreover, AdaICL poses its sampling strategy as a Maximum Coverage
problem, that dynamically adapts based on the model's feedback and can be
approximately solved via greedy algorithms. Extensive experiments on nine
datasets and seven LLMs show that AdaICL improves performance by 4.4% accuracy
points over SOTA (7.7% relative improvement), is up to 3x more budget-efficient
than performing annotations uniformly at random, while it outperforms SOTA with
2x fewer ICL examples.

通过提出 AdaICL 算法，本研究在大型语言模型中探索了一种基于主动学习的方法，通过优化自适应的示例选择策略，提高了性能和预算效率。