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
Shuai Fan, Jinping Gou, Yang Li, Jiaxing Bai, Chen Lin, Wanxian Guan, Xubin Li, Hongbo Deng, Jian Xu, Bo ZhengAuthors Info & ClaimsPages 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.
References
[1]
Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu, and Bin Wang. 2022. DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation. In SIGIR. 267--277.
[2]
Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR (Poster).
[3]
Yuting Chen, Yanshi Wang, Yabo Ni, Anxiang Zeng, and Lanfen Lin. 2020a. Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce. In ICDM (Workshops). 127--135.
[4]
Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020b. Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance. In SIGIR. 579--588.
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). 4171--4186.
[6]
Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019. Metapath-Guided Heterogeneous Graph Neural Network for Intent Recommendation. 2478--2486.
[7]
Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, and Xiaoyan Zhu. 2018. Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning. In Proceedings of the 2018 World Wide Web Conference. 1939--1948.
[8]
Yulong Gu, Wentian Bao, Dan Ou, Xiang Li, Baoliang Cui, Biyu Ma, Haikuan Huang, Qingwen Liu, and Xiaoyi Zeng. 2021. Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce. In CIKM. 3828--3837.
[9]
Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Xin-Shun Xu, and Mohan Kankanhalli. 2018. Multi-modal preference modeling for product search. In Proceedings of the 26th ACM international conference on Multimedia. 1865--1873.
[10]
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034.
[11]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.
[12]
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial Personalized Ranking for Recommendation. In SIGIR. 355--364.
[13]
SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users. In CIKM. 1563--1572.
[14]
Pengcheng Li, Runze Li, Qing Da, Anxiang Zeng, and Lijun Zhang. 2020. Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space. In CIKM. 2605--2612.
[15]
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018. Adaptive Graph Convolutional Neural Networks. In AAAI. 3546--3553.
[16]
Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020. Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. In CIKM. 885--894.
[17]
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H. Chi. 2018. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. In KDD. 1930--1939.
[18]
Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020. Disentangled Self-Supervision in Sequential Recommenders. In KDD. 483--491.
[19]
Yu A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence, Vol. 42, 4 (2018), 824--836.
[20]
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-Domain Recommendation: An Embedding and Mapping Approach. In IJCAI. 2464--2470.
[21]
Xichuan Niu, Bofang Li, Chenliang Li, Jun Tan, Rong Xiao, and Hongbo Deng. 2021. Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks. In WSDM. 1038--1046.
[22]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint (2018). https://arxiv.org/abs/1807.03748
[23]
Qijie Shen, Wanjie Tao, Jing Zhang, Hong Wen, Zulong Chen, and Quan Lu. 2021. SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios. In CIKM. 4094--4103.
[24]
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu. 2021. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In CIKM. 4104--4113.
[25]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. stat, Vol. 1050 (2017), 20.
[26]
Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, and Shaoping Ma. 2022b. Towards Representation Alignment and Uniformity in Collaborative Filtering. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1816--1825.
[27]
Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, and Xiao-Ming Wu. 2020b. M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems. In KDD. 2349--2358.
[28]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020a. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010.
[29]
Yichao Wang, Huifeng Guo, Bo Chen, Weiwen Liu, Zhirong Liu, Qi Zhang, Zhicheng He, Hongkun Zheng, Weiwei Yao, Muyu Zhang, Zhenhua Dong, and Ruiming Tang. 2022a. CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation. In KDD. 4090--4099.
[30]
Max Welling and Thomas N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Representations (ICLR 2017).
[31]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. 726--735.
[32]
Tao Wu, Ellie Ka In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John R. Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, and Pei Cao. 2020. Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval. In CIKM. 2821--2828.
[33]
Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2022. Contrastive Cross-domain Recommendation in Matching. In KDD. 4226--4236.
[34]
Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, and Guihai Chen. 2022. Cross-Task Knowledge Distillation in Multi-Task Recommendation. In AAAI. 4318--4326.
[35]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation. In SIGIR. 1294--1303.
[36]
Jiejie Zhao, Bowen Du, Leilei Sun, Fuzhen Zhuang, Weifeng Lv, and Hui Xiong. 2019. Multiple Relational Attention Network for Multi-task Learning. In KDD. 1123--1131.
[37]
Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, and Aixin Sun. 2022. Automatic Expert Selection for Multi-Scenario and Multi-Task Search. In SIGIR. 1535--1544.
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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