Collaboration or Competition: An Infomax-Based Period-Aware Transformer for Ticket-Grabbing Prediction

Helping users to grab train tickets during a travel peak is a very important service provided by many mainstream online travel platforms (OTPs), e.g., booking.com, Ctrip.com, and Alibaba Fliggy, which greatly enriches the experience for platform users. To optimize such train ticket-grabbing service, a vital accompanying task is to predict the train ticket-grabbing success rates for users during their train ticket-grabbing process to help them make decisions. Although many endeavours have been made towards the traffic prediction problem, none of them was dedicated to solving the ticket-grabbing issue. That is, prior methods ignored the unique properties exhibited in the ticket-grabbing scenario, such as the specific spatial relationship between stations and trains, the collaboration and competition relationships between different routes, and the temporal periodic pattern in ticket-grabbing. In this paper, we propose a novel Infomax-based Period-aware Transformer (IPT) tailored for predicting the success rate of train ticket-grabbing that will be displayed on OTPs, which is to our best knowledge the first attempt along this line. IPT contains three modules: i) a multi-view node embedding module, which serves to model the special spatial relationships between stations and trains by employing the intra- and inter-graph aggregation layers; ii) an infomax-based graph representation learning module, which aims to learn a high-level node embedding by training a discriminator to distinguish different types of edges in the route graph; iii) a period-aware Transformer module, which intends to discover the ticket-grabbing temporal periodic dependencies by designing a periodic activation function. Extensive offline and online evaluations on a real-world dataset show that IPT substantially outperforms state-of-the-art baselines.