[3E6-GS-10] AI application:

[3E6-GS-10-03] User-Level Hotel Booking Prediction for Online Travel Agencies

〇Hiroki Yasuda¹, Kyohei Atarashi¹, Koh Takeuchi¹, Hisashi Kashima¹, Ryo Matsui², Shusaku Yoshizumi², Shunji Umetani², Toma Suzuki² (1. Univ. of Kyoto, 2. Recruit Co., Ltd.)

Keywords:machine learning, calibration, online travel agency

In recent years, with the spread of the Internet, more and more people are booking hotels online. In particular, online travel agencies have simplified hotel bookings by providing convenient functions, and some services now have a large number of users. For such a large-scale service, it is important not only to acquire new users, but also to maintain relationships with existing users in order to gain long-term profits. However, a one-size-fits-all approach to all users is inefficient. In this study, we tested a method to predict when a user will make his/her next booking based on the user's past booking and cancellation history and demographic information, and to appropriately estimate the probability of such a booking. The results of evaluating multiple models and probability calibration methods using real-world data showed differences in prediction accuracy and probability calibration effects among the methods, but also suggested that further study is needed to improve performance, including recall rates.