mlsanityData to 14 Oct 2026

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

arXiv 2207.13329 ↗First posted 27 Jul 2022cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

21 of 40 references.

References · 21

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9 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 3.

5 papers.

  1. 2023Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devicescited by 6
  2. 2025TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systemscited by 4
  3. 2026Delayed Feedback Modeling for Post-Click Gross Merchandise Volume Prediction: Benchmark, Insights and Approachescited by 2
  4. 2026EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledgecited by 1
  5. 2026Coden: Efficient Temporal Graph Neural Networks for Continuous Predictioncited by 0