# Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace

arXiv: https://arxiv.org/abs/2302.11052 · HTML: https://mlsanity.com/paper/2302.11052/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [On the Effectiveness of Sampled Softmax Loss for Item Recommendation](https://mlsanity.com/paper/2201.02327/) (2022)
- [CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval](https://mlsanity.com/paper/2202.07247/) (2022)
- [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](https://mlsanity.com/paper/2110.15154/) (2021)
- [Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search](https://mlsanity.com/paper/2202.06212/) (2022)
- [Image Search with Text Feedback by Additive Attention Compositional Learning](https://mlsanity.com/paper/2203.03809/) (2022)
- [An Embedding-Based Grocery Search Model at Instacart](https://mlsanity.com/paper/2209.05555/) (2022)

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

- [Large Language Models for Relevance Judgment in Product Search](https://mlsanity.com/paper/2406.00247/) (2024)
- [Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models](https://mlsanity.com/paper/2505.07105/) (2025)
- [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)
- [Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation](https://mlsanity.com/paper/2504.11284/) (2025)
- [Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation](https://mlsanity.com/paper/2505.16065/) (2025)
- [Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search](https://mlsanity.com/paper/2510.20567/) (2025)
- [Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search](https://mlsanity.com/paper/2603.15262/) (2026)
- [Efficient GPU Retrieval for Semantic Search](https://mlsanity.com/paper/2608.28968/) (2026)

## Abstract (arXiv)

Embedding-based Retrieval (EBR) in e-commerce search is a powerful search retrieval technique to address semantic matches between search queries and products. However, commercial search engines like Facebook Marketplace Search are complex multi-stage systems optimized for multiple business objectives. At Facebook Marketplace, search retrieval focuses on matching search queries with relevant products, while search ranking puts more emphasis on contextual signals to up-rank the more engaging products. As a result, the end-to-end searcher experience is a function of both relevance and engagement, and the interaction between different stages of the system. This presents challenges to EBR systems in order to optimize for better searcher experiences. In this paper we presents Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations. Que2Engage takes a multimodal & multitask approach to infuse contextual information into the retrieval stage and to balance different business objectives. We show the effectiveness of our approach via a multitask evaluation framework and thorough baseline comparisons and ablation studies. Que2Engage is deployed on Facebook Marketplace Search and shows significant improvements in searcher engagement in two weeks of A/B testing.