Prediction of Willingness to Pay for Airline Seat Selection Based on Improved Ensemble Learning

Airlines have launched various ancillary services to meet their passengers’ requirements and to increase their revenue. Ancillary revenue from seat selection is an important source of revenue for airlines and is a common type of advertisement. However, advertisements are generally delivered to all customers, including a significant proportion of people who do not wish to pay for seat selection. Random advertisements may thus decrease the amount of profit generated since users will tire of useless advertising, leading to a decrease in user stickiness. To solve this problem, we propose a Bagging in Certain Ratio Light Gradient Boosting Machine (BCR-LightGBM) to predict the willingness of passengers to pay to choose their seats. The experimental results show that the proposed model outperforms all 12 comparison models in terms of the area under the receiver operating characteristic curve (ROC-AUC) and F1-score. Furthermore, we studied two typical samples to demonstrate the decision-making phase of a decision tree in BCR-LightGBM and applied the Shapley additive explanation (SHAP) model to analyse the important influencing factors to further enhance the interpretability. We conclude that the customer’s values, the ticket fare, and the length of the trip are three factors that airlines should consider in their seat selection service.