HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace
| العنوان: | HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace |
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| المؤلفون: | Yunzhong He, Cong Zhang, Ruoyan Kong, Chaitanya Kulkarni, Qing Liu, Ashish Gandhe, Amit Nithianandan, Arul Prakash |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), Machine Learning (cs.LG), Computer Science - Information Retrieval |
| الوصف: | Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages. Its deployment also needs to consider challenges in scalability and downstream integration in order to translate modeling advances into better search result relevance. In this paper we present HierCat, the query categorization system at Facebook Marketplace. HierCat addresses these challenges by leveraging multi-task pre-training of dual-encoder architectures with a hierarchical inference step to effectively learn from weakly supervised training data mined from searcher engagement. We show that HierCat not only outperforms popular methods in offline experiments, but also leads to 1.4% improvement in NDCG and 4.3% increase in searcher engagement at Facebook Marketplace Search in online A/B testing. Accepted by WWW'2023 |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b25739eb67e7220ecd68bd3f0e90773 http://arxiv.org/abs/2302.10527 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....8b25739eb67e7220ecd68bd3f0e90773 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |