Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
This work addresses occupancy prediction for hotel dynamic pricing platforms, offering an incremental improvement by modeling price elasticity to enhance accuracy.
The paper tackles the problem of accurately predicting hotel occupancy for dynamic pricing by proposing a novel demand function that explicitly models price elasticity, and demonstrates superior performance over state-of-the-art baselines in experiments on real-world datasets.
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.