Abstract
In this paper, we present a novel Graph Attention Network based framework for GMV (Gross Merchandise Volume) forecast on online festival, called GAT-GF. Based on the well-designed retailer-customer graph and retailer-retailer graph, we employ a graph neural network based encoder cooperated with multi-head attention and self attention mechanism to comprehensively capture complicated structure between consumers and retailers, followed by a two-way regression decoder for effective predition. Extensive experiments on real promotion datasets demonstrate the superiority of GAT-GF.
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Yu, Q. et al. (2021). A Graph Attention Network Model for GMV Forecast on Online Shopping Festival. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_11
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DOI: https://doi.org/10.1007/978-3-030-85896-4_11
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