@inproceedings{lu-etal-2021-graph,
title = "Graph-based Multilingual Product Retrieval in {E}-Commerce Search",
author = "Lu, Hanqing and
Hu, Youna and
Zhao, Tong and
Wu, Tony and
Song, Yiwei and
Yin, Bing",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.19",
doi = "10.18653/v1/2021.naacl-industry.19",
pages = "146--153",
abstract = "Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing with maintaining per-country specific e-commerce search systems, having an universal system across countries can further reduce the operational and computational costs, and facilitate business expansion to new countries. In this paper, we introduce an universal end-to-end multilingual retrieval system, and discuss our learnings and technical details when training and deploying the system to serve billion-scale product retrieval for e-commerce search. In particular, we propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models and graph neural network architectures to capture the interactions between search queries and items in e-commerce search. Offline experiments on five countries data show that our algorithm outperforms the state-of-the-art baselines by 35{\%} recall and 25{\%} mAP on average. Moreover, the proposed model shows significant increase of conversion/revenue in online A/B experiments and has been deployed in production for multiple countries.",
}
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<abstract>Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing with maintaining per-country specific e-commerce search systems, having an universal system across countries can further reduce the operational and computational costs, and facilitate business expansion to new countries. In this paper, we introduce an universal end-to-end multilingual retrieval system, and discuss our learnings and technical details when training and deploying the system to serve billion-scale product retrieval for e-commerce search. In particular, we propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models and graph neural network architectures to capture the interactions between search queries and items in e-commerce search. Offline experiments on five countries data show that our algorithm outperforms the state-of-the-art baselines by 35% recall and 25% mAP on average. Moreover, the proposed model shows significant increase of conversion/revenue in online A/B experiments and has been deployed in production for multiple countries.</abstract>
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%0 Conference Proceedings
%T Graph-based Multilingual Product Retrieval in E-Commerce Search
%A Lu, Hanqing
%A Hu, Youna
%A Zhao, Tong
%A Wu, Tony
%A Song, Yiwei
%A Yin, Bing
%Y Kim, Young-bum
%Y Li, Yunyao
%Y Rambow, Owen
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F lu-etal-2021-graph
%X Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing with maintaining per-country specific e-commerce search systems, having an universal system across countries can further reduce the operational and computational costs, and facilitate business expansion to new countries. In this paper, we introduce an universal end-to-end multilingual retrieval system, and discuss our learnings and technical details when training and deploying the system to serve billion-scale product retrieval for e-commerce search. In particular, we propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models and graph neural network architectures to capture the interactions between search queries and items in e-commerce search. Offline experiments on five countries data show that our algorithm outperforms the state-of-the-art baselines by 35% recall and 25% mAP on average. Moreover, the proposed model shows significant increase of conversion/revenue in online A/B experiments and has been deployed in production for multiple countries.
%R 10.18653/v1/2021.naacl-industry.19
%U https://aclanthology.org/2021.naacl-industry.19
%U https://doi.org/10.18653/v1/2021.naacl-industry.19
%P 146-153
Markdown (Informal)
[Graph-based Multilingual Product Retrieval in E-Commerce Search](https://aclanthology.org/2021.naacl-industry.19) (Lu et al., NAACL 2021)
ACL
- Hanqing Lu, Youna Hu, Tong Zhao, Tony Wu, Yiwei Song, and Bing Yin. 2021. Graph-based Multilingual Product Retrieval in E-Commerce Search. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 146–153, Online. Association for Computational Linguistics.