Predicting Airline Additional Services Consumption Willingness Based on High-Dimensional Incomplete Data

Abstract

Prediction of the purchase willingness of passengers has great benefits for airlines to
promote auxiliary services. However, the datasets stored in passenger travel information systems
are often high-dimensional and incomplete. This study develops a prediction method of airline
additional service consumption willingness based on high-dimensional and incomplete datasets
with a triple-layer hybrid PSO-XGBoost model, which consists of an incomplete data processing
layer, a high-dimensional data processing layer, and a predicting layer. The raw dataset is
converted into a complete and low-dimensional dataset through the first two layers and inputted
into the predicting layer to train and optimize the XGBoost model together with the PSO
algorithm and 10-fold cross-validation. The experimental results show that the proposed method
outperforms other traditional machine learning models, presenting the highest prediction score
with 0.9879 in terms of AUC. The findings help predict airline additional services consumption
intentions of passengers and are beneficial to efficient and low-cost precise marketing for airlines