BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network
Authors: 
Shuai Fan, Jinping Gou, Yang Li, Jiaxing Bai, Chen Lin, Wanxian Guan, Xubin Li, Hongbo Deng, Jian Xu, Bo ZhengAuthors Info & Claims
Pages 514 - 523
Abstract
Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.
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Index Terms
- BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network
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Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Published: 21 October 2023
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