A Graph Attention Network Model for GMV Forecast on Online Shopping Festival
| Autor: | Xingyu Zhong, Ya-Lin Zhang, Kai Huang, Binbin Hu, Zhiqiang Zhang, Yanming Fang, Jun Zhou, Ziqi Liu, Qianyu Yu, Shuo Yang |
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| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
Computer science Graph neural networks business.industry media_common.quotation_subject Self attention Volume (computing) Machine learning computer.software_genre Promotion (rank) Attention network Graph (abstract data type) Artificial intelligence business computer Encoder media_common |
| Zdroj: | Web and Big Data ISBN: 9783030858957 APWeb/WAIM (1) |
| DOI: | 10.1007/978-3-030-85896-4_11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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