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

arXiv: https://arxiv.org/abs/2302.10527 · HTML: https://mlsanity.com/paper/2302.10527/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization](https://mlsanity.com/paper/2104.11384/) (2021)

## Built on by (most cited first, 9 of 9)

- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems](https://mlsanity.com/paper/2306.11279/) (2023)
- [On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework](https://mlsanity.com/paper/2308.03538/) (2023)
- [Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models](https://mlsanity.com/paper/2306.07455/) (2023)
- [Hierarchical Query Classification in E-commerce Search](https://mlsanity.com/paper/2403.06021/) (2024)
- [Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation](https://mlsanity.com/paper/2505.16065/) (2025)
- [Organizational Bulk Email Systems: Their Role and Performance in Remote Work](https://mlsanity.com/paper/2308.05085/) (2023)
- [Hierarchical Job Classification with Similarity Graph Integration](https://mlsanity.com/paper/2507.09949/) (2025)

## Abstract (arXiv)

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.