Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization
Shansan Gong, Zelin Zhou, Shuo Wang, Fengjiao Chen, Xiujie Song, Xuezhi Cao, Yunsen Xian, Kenny Zhu
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.
BibTeX
@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"
}