  Event ticket price prediction is important to marketing strategy for any
sports team or musical ensemble. An accurate prediction model can help the
marketing team to make promotion plan more effectively and efficiently.
However, given all the historical transaction records, it is challenging to
predict the sale price of the remaining seats at any future timestamp, not only
because that the sale price is relevant to a lot of features (seat locations,
date-to-event of the transaction, event date, team performance, etc.), but also
because of the temporal and spatial sparsity in the dataset. For a
game/concert, the ticket selling price of one seat is only observable once at
the time of sale. Furthermore, some seats may not even be purchased (therefore
no record available). In fact, data sparsity is commonly encountered in many
prediction problems. Here, we propose a bi-level optimizing deep neural network
to address the curse of spatio-temporal sparsity. Specifically, we introduce
coarsening and refining layers, and design a bi-level loss function to
integrate different level of loss for better prediction accuracy. Our model can
discover the interrelations among ticket sale price, seat locations, selling
time, event information, etc. Experiments show that our proposed model
outperforms other benchmark methods in real-world ticket selling price
prediction.
