Graph-based Multilingual Product Retrieval in E-commerce Search
- URL: http://arxiv.org/abs/2105.02978v1
- Date: Thu, 6 May 2021 21:49:10 GMT
- Title: Graph-based Multilingual Product Retrieval in E-commerce Search
- Authors: Hanqing Lu, Youna Hu, Tong Zhao, Tony Wu, Yiwei Song, Bing Yin
- Abstract summary: We introduce a universal end-to-end multilingual retrieval system to serve billion-scale product retrieval for e-commerce search.
We propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models.
Our algorithm outperforms the state-of-the-art baselines by 35% recall and 25% mAP on average.
- Score: 29.156647795471176
- License: http://creativecommons.org/licenses/by/4.0/
- 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 a universal system across countries
can further reduce the operational and computational costs, and facilitate
business expansion to new countries. In this paper, we introduce a 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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