Many hotels target guest acquisition efforts to specific markets in order to
best anticipate individual preferences and needs of their guests. Likewise,
such strategic positioning is a prerequisite for efficient marketing budget
allocation. Official statistics report on the number of visitors from different
countries, but no fine-grained information on the guest composition of
individual businesses exists. There is, however, growing interest in such data
from competitors, suppliers, researchers and the general public. We demonstrate
how machine learning can be leveraged to extract references to guest
nationalities from unstructured text reviews in order to dynamically assess and
monitor the dynamics of guest composition of individual businesses. In
particular, we show that a rather simple architecture of pre-trained embeddings
and stacked LSTM layers provides a better performance-runtime tradeoff than
more complex state-of-the-art language models.

利用机器学习技术从非结构化的文本评论中提取客人国籍参考，以动态评估和监测各个企业的客人构成。在研究中，使用预训练嵌入和堆叠的 LSTM 层的简单架构，证明了性能和运行时间之间的良好平衡。

酒店评论中评估客人国籍构成

Assessing Guest Nationality Composition from Hotel Reviews

While marketing budget allocation has been studied for decades in traditional
business, nowadays online business brings much more challenges due to the
dynamic environment and complex decision-making process. In this paper, we
present a novel unified framework for marketing budget allocation. By
leveraging abundant data, the proposed data-driven approach can help us to
overcome the challenges and make more informed decisions. In our approach, a
semi-black-box model is built to forecast the dynamic market response and an
efficient optimization method is proposed to solve the complex allocation task.
First, the response in each market-segment is forecasted by exploring
historical data through a semi-black-box model, where the capability of logit
demand curve is enhanced by neural networks. The response model reveals
relationship between sales and marketing cost. Based on the learned model,
budget allocation is then formulated as an optimization problem, and we design
efficient algorithms to solve it in both continuous and discrete settings.
Several kinds of business constraints are supported in one unified optimization
paradigm, including cost upper bound, profit lower bound, or ROI lower bound.
The proposed framework is easy to implement and readily to handle large-scale
problems. It has been successfully applied to many scenarios in Alibaba Group.
The results of both offline experiments and online A/B testing demonstrate its
effectiveness.

本文提出了一种基于数据驱动、使用半黑盒模型和高效优化算法的营销预算配置框架，以应对在线业务中动态环境和复杂决策 - making 过程，支持多种业务约束，可以处理大规模的问题，并在阿里巴巴集团的许多场景中取得了成功的应用，离线实验和在线 A/B 测试均证明了其有效性。