论文标题
建模酒店动态定价的占用预测价格弹性
Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
论文作者
论文摘要
需求估计在动态定价中起着重要的作用，在动态定价中，可以通过根据需求曲线最大化收入来获得最佳价格。在在线酒店预订平台中，房间的需求或居住率随着房间类型而变化，随着时间的推移变化，因此获得准确的占用估算是一项挑战。在本文中，我们提出了一种新型的酒店需求功能，该功能明确地模拟了对入住预测需求需求的价格弹性，并设计了一个价格弹性预测模型，以了解各种影响因素的动态价格弹性系数。我们的模型由精心设计的弹性学习模块组成，以减轻内生性问题，并在多任务框架中接受培训以解决数据稀疏性。我们对现实世界数据集进行了全面的实验，并验证了我们方法比最先进的基准的优越性，以实现占用预测和动态定价。
Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve. In online hotel booking platform, the demand or occupancy of rooms varies across room-types and changes over time, and thus it is challenging to get an accurate occupancy estimate. In this paper, we propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction, and design a price elasticity prediction model to learn the dynamic price elasticity coefficient from a variety of affecting factors. Our model is composed of carefully designed elasticity learning modules to alleviate the endogeneity problem, and trained in a multi-task framework to tackle the data sparseness. We conduct comprehensive experiments on real-world datasets and validate the superiority of our method over the state-of-the-art baselines for both occupancy prediction and dynamic pricing.
