@inproceedings{gong-etal-2023-transferable,
title = "Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization",
author = "Gong, Shansan and
Zhou, Zelin and
Wang, Shuo and
Chen, Fengjiao and
Song, Xiujie and
Cao, Xuezhi and
Xian, Yunsen and
Zhu, Kenny",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.46",
doi = "10.18653/v1/2023.acl-industry.46",
pages = "476--486",
abstract = "As e-commerce platforms develop different business lines, a special but challenging product categorization scenario emerges, where there are multiple domain-specific category taxonomies and each of them evolves dynamically over time. In order to unify the categorization process and ensure efficiency, we propose a two-stage taxonomy-agnostic framework that relies solely on calculating the semantic relatedness between product titles and category names in the vector space. To further enhance domain transferability and better exploit cross-domain data, we design two plug-in modules: a heuristic mapping scorer and a pretrained contrastive ranking module with the help of meta concepts, which represent keyword knowledge shared across domains. Comprehensive offline experiments show that our method outperforms strong baselineson three dynamic multi-domain product categorization (DMPC) tasks,and online experiments reconfirm its efficacy with a5{\%} increase on seasonal purchase revenue. Related datasets will be released.",
}
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<abstract>As e-commerce platforms develop different business lines, a special but challenging product categorization scenario emerges, where there are multiple domain-specific category taxonomies and each of them evolves dynamically over time. In order to unify the categorization process and ensure efficiency, we propose a two-stage taxonomy-agnostic framework that relies solely on calculating the semantic relatedness between product titles and category names in the vector space. To further enhance domain transferability and better exploit cross-domain data, we design two plug-in modules: a heuristic mapping scorer and a pretrained contrastive ranking module with the help of meta concepts, which represent keyword knowledge shared across domains. Comprehensive offline experiments show that our method outperforms strong baselineson three dynamic multi-domain product categorization (DMPC) tasks,and online experiments reconfirm its efficacy with a5% increase on seasonal purchase revenue. Related datasets will be released.</abstract>
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%0 Conference Proceedings
%T Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization
%A Gong, Shansan
%A Zhou, Zelin
%A Wang, Shuo
%A Chen, Fengjiao
%A Song, Xiujie
%A Cao, Xuezhi
%A Xian, Yunsen
%A Zhu, Kenny
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gong-etal-2023-transferable
%X As e-commerce platforms develop different business lines, a special but challenging product categorization scenario emerges, where there are multiple domain-specific category taxonomies and each of them evolves dynamically over time. In order to unify the categorization process and ensure efficiency, we propose a two-stage taxonomy-agnostic framework that relies solely on calculating the semantic relatedness between product titles and category names in the vector space. To further enhance domain transferability and better exploit cross-domain data, we design two plug-in modules: a heuristic mapping scorer and a pretrained contrastive ranking module with the help of meta concepts, which represent keyword knowledge shared across domains. Comprehensive offline experiments show that our method outperforms strong baselineson three dynamic multi-domain product categorization (DMPC) tasks,and online experiments reconfirm its efficacy with a5% increase on seasonal purchase revenue. Related datasets will be released.
%R 10.18653/v1/2023.acl-industry.46
%U https://aclanthology.org/2023.acl-industry.46
%U https://doi.org/10.18653/v1/2023.acl-industry.46
%P 476-486
Markdown (Informal)
[Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization](https://aclanthology.org/2023.acl-industry.46) (Gong et al., ACL 2023)
ACL
- Shansan Gong, Zelin Zhou, Shuo Wang, Fengjiao Chen, Xiujie Song, Xuezhi Cao, Yunsen Xian, and Kenny Zhu. 2023. Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 476–486, Toronto, Canada. Association for Computational Linguistics.