  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.
