Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization

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 plugin 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 baselines on three dynamic multi-domain product categorization (DMPC) tasks, and online experiments reconfirm its efficacy with a 5% increase on seasonal purchase revenue. Related datasets are released 1 .