Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce

TL;DR

This paper introduces Gaia, a graph neural network with temporal shift aware attention, designed to improve gross merchandise value (GMV) forecasting in e-commerce by leveraging temporal and relational data, showing superior performance on real datasets.

Contribution

Gaia is the first GNN model with temporal shift aware attention specifically tailored for GMV prediction in e-commerce, integrating temporal dependencies and neighbor correlations.

Findings

Gaia outperforms baseline models on Alipay's real dataset.

Gaia achieves significant improvements in GMV forecasting accuracy.

Deployment in a simulated online environment confirms Gaia's practical effectiveness.

Abstract

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.