Prices Do Matter: Modeling Price Competitiveness for Online Hotel Industry

Broad adoption of Online Travel Platforms (OTPs) has led to increasing interest in accurately predicting users' hotel purchase behavior, with price being a key influencer in user decision-making and receiving significant focus. In examining the hotel purchasing process, we identify a pervasive trend that users make extensive price comparisons before making decisions. Existing research primarily focuses on a hotel's own price, neglecting the complex dynamics of market-driven price competition. In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel's pricing competitiveness within the marketplace. Being independent of specific user preferences, MHPC can be applied to and improve various downstream operations in the online hotel industry, such as hotel ranking and pricing, ultimately benefiting hoteliers, users, and OTPs. Furthermore, a novel Hotel Price Competitiveness-aware Purchase Prediction Model (HP3M) is constructed by incorporating MHPC and demand dynamics into a multi-task learning framework, featuring three distinct submodules to encompass the tri-dimensional facets of MHPC. Extensive offline and online experiments demonstrate HP3M's effectiveness in predicting hotel purchase probability and enhancing the performance of hotel ranking and pricing compared to the state-of-the-art methods. HP3M has been fully deployed on Fliggy, a leading OTP in China, serving thousands of hoteliers and tens of millions of users.