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.

本文介绍了一种基于嵌入式检索的电子商务搜索技术，提出了一种多模态、多任务的搜索嵌入式检索系统，称为 Que2Engage，旨在缩小检索与排名之间的差距以及在平衡不同商业目标的同时提供更好的搜索体验。该方案在 Facebook Marketplace Search 上实施后，两周内的 A/B 测试结果表明，搜索器的参与度有了显著性的提升。