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.

翻译：嵌入检索（EBR）作为电商搜索中的关键技术，旨在解决搜索查询与商品之间的语义匹配问题。然而，诸如Facebook Marketplace搜索等商业搜索引擎是面向多重业务目标优化的复杂多阶段系统：在Facebook Marketplace中，搜索检索阶段侧重于匹配搜索查询与相关商品，而排序阶段则更强调上下文信号以提升高互动商品的排序位置。因此，端到端的搜索体验由相关性与互动性共同决定，且受系统不同阶段间交互作用的影响。这给EBR系统优化搜索体验带来了挑战。本文提出Que2Engage系统——一种为弥合检索与排序阶段鸿沟、实现端到端优化而构建的搜索EBR系统。Que2Engage采用多模态多任务方法，将上下文信息融入检索阶段，并平衡不同业务目标。我们通过多任务评估框架、全面的基线对比及消融研究验证了该方法的有效性。Que2Engage已在Facebook Marketplace搜索中部署，两周A/B测试显示其在用户互动指标上取得显著提升。