Passenger Payment Willingness Prediction by Static and Dynamic Multi-dimensional Ticket Attributes Fusion

Ticket pricing is always a challenging problem for world-wide airline companies when balancing their revenues and sales, where tickets are often discounted to adapt to a marketable price level. In this paper, we transform the problem of modeling Passenger Payment Willingness (PPW) into a top-$K$ recommendation problem, where a list of ticket discounted ratios is recommended by fully considering ticket discount histories of peer airline companies and multi-dimensional ticket attributes, i.e., passenger purchasing capability. We propose a novel deep model, called “NCL”, which integrates N-Beats, a Graph Convolutional Neural Network (GCN) and an LSTM together to model temporal variations of ticket discounts and complex relationships among multi-dimensional ticket attributes. Specifically, first, the ticket discount historical sequence is integrated by N-Beats. Then, multi-dimensional ticket attributes are divided into dynamic and static categories, where an attribute graph of static attributes is constructed, and a GCN is leveraged to extract features from it. After, LSTM is used to perform temporal feature fusion on the dynamic attributes. Finally, NCL integrates features from all the above and predicts future ticket discounts. Experiments confirm that the prediction accuracy of NCL is more than 60% in terms of ACC@1.