  E-commerce has gone a long way in empowering merchants through the internet.
In order to store the goods efficiently and arrange the marketing resource
properly, it is important for them to make the accurate gross merchandise value
(GMV) prediction. However, it's nontrivial to make accurate prediction with the
deficiency of digitized data. In this article, we present a solution to better
forecast GMV inside Alipay app. Thanks to graph neural networks (GNN) which has
great ability to correlate different entities to enrich information, we propose
Gaia, a graph neural network (GNN) model with temporal shift aware attention.
Gaia leverages the relevant e-seller' sales information and learn neighbor
correlation based on temporal dependencies. By testing on Alipay's real dataset
and comparing with other baselines, Gaia has shown the best performance. And
Gaia is deployed in the simulated online environment, which also achieves great
improvement compared with baselines.
