@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.",
}

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