RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues

Predicting locational choices (i.e., where one chooses to sit) is a challenging task because preferences are highly heterogeneous and depend not only on the location of the seats in the environment but also on the location of others. In the present research, we propose RecSeats - a framework to predict locational choices. The framework augments individual-level discrete choice models with a convolutional neural network (CNN) which can capture higher order interactions between features of available seats. The framework is flexible and can accommodate complexity in real-world locational choice data such as variability in the number of tickets purchased and the number and locations from past purchases. Applied to both locational choice experiment data and to ticketing data from a large North-American concert hall, we show that augmenting individual-level discrete choice models with a CNN consistently provides strong predictive accuracy.