Auxiliary service recommendation for online flight booking

Abstract

Booking flights through online travel companies (OTCs) is becoming increasingly popular. In order to improve profits, OTCs often suggest additional optional auxiliary services, such as security insurance, a VIP lounge or a pick-up service, to passengers. In order to promote the sale of auxiliary services, these can be selected as a default when passengers purchase a flight. However, if a passenger does not want to buy these services, he will have to cancel them himself, which can result in a negative user experience. Therefore, a personalized auxiliary service recommendation approach is proposed (IR-GBDT), which is built on the Gradient Boosting Decision Tree (GBDT) model. GBDT is also applied to mine the interrelationships between services so that a service package is finally recommended. The experiments on a real dataset which includes 6-month’s of flight order data shows that our model has improved performance compared to the others. abstract environment.