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.

本文提出了一种名为 HierCat 的查询分类系统，该系统通过使用双编码器体系结构的多任务预训练和分层推理步骤来从弱监督训练数据中有效地学习，以解决 Facebook Marketplace 等客户对客户电子商务平台上查询分类的挑战。实验结果表明，HierCat 不仅在离线实验中优于常用方法，而且在实际应用中，Facebook Marketplace Search 的 NDCG 指标有了 1.4% 的提升，并且搜索者的参与度增加了 4.3%。

HierCat: 在 Facebook Marketplace 上利用弱监督数据进行层级化查询分类

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

Query categorization is an essential part of query intent understanding in
e-commerce search. A common query categorization task is to select the relevant
fine-grained product categories in a product taxonomy. For frequent queries,
rich customer behavior (e.g., click-through data) can be used to infer the
relevant product categories. However, for more rare queries, which cover a
large volume of search traffic, relying solely on customer behavior may not
suffice due to the lack of this signal. To improve categorization of rare
queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the
latent knowledge embedded in semantically or lexically similar product
documents to enrich the representation of the more rare queries. To this end,
we propose a novel deep neural model named Attentive Pseudo Relevance Feedback
Network (APRF-Net) to enhance the representation of rare queries for query
categorization. To demonstrate the effectiveness of our approach, we collect
search queries from a large commercial search engine, and compare APRF-Net to
state-of-the-art deep learning models for text classification. Our results show
that the APRF-Net significantly improves query categorization by 5.9% on F1@1
score over the baselines, which increases to 8.2% improvement for the rare
(tail) queries. The findings of this paper can be leveraged for further
improvements in search query representation and understanding.

本文提出了一种名为 APRF-Net 的神经模型，将伪相关反馈（PRF）方法应用于查询分类中，以改善较为罕见的查询的表现，通过实验结果表明，这种模型可以显著提高查询分类效果。