HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

Autor:	He, Yunzhong, Zhang, Cong, Kong, Ruoyan, Kulkarni, Chaitanya, Liu, Qing, Gandhe, Ashish, Nithianandan, Amit, Prakash, Arul
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1145/3543873.3584622
Popis:	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. Comment: Accepted by WWW'2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.10527 Zobrazit plný text záznamu View this record from Arxiv