  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.
