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

TL;DR

Que2Engage is an embedding-based retrieval system designed for Facebook Marketplace that integrates contextual signals to improve relevance and engagement, demonstrating significant positive impact in live A/B tests.

Contribution

It introduces a multimodal, multitask retrieval approach that bridges the gap between relevance and engagement in e-commerce search systems.

Findings

Significant improvements in searcher engagement observed in A/B testing.

Effective integration of contextual information into retrieval stage.

Robust baseline and ablation studies validate the approach.

Abstract

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.