In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement, and $>5\%$ ads CTR in Pinterest's production search system. The main contributors to these gains are improved content understanding, better multi-task learning, and real-time serving. We enrich our entity representations using diverse text derived from image captions from a generative LLM, historical engagement, and user-curated boards. Our multitask learning setup produces a single search query embedding in the same space as pin and product embeddings and compatible with pre-existing pin and product embeddings. We show the value of each feature through ablation studies, and show the effectiveness of a unified model compared to standalone counterparts. Finally, we share how these embeddings have been deployed across the Pinterest search stack, from retrieval to ranking, scaling to serve $300k$ requests per second at low latency. Our implementation of this work is available at https://github.com/pinterest/atg-research/tree/main/omnisearchsage.

翻译：本文提出了OmniSearchSage，一个用于Pinterest搜索中理解搜索查询、图钉和产品的通用可扩展系统。我们联合学习了统一查询嵌入与图钉嵌入及产品嵌入，使Pinterest生产搜索系统的相关性提升超过8%、用户互动提升超过7%、广告点击率提升超过5%。这些增益主要归因于内容理解的改进、多任务学习的优化以及实时服务能力的提升。我们利用生成式大语言模型生成的图像描述文本、历史用户交互数据以及用户策展画板中的多样化文本，丰富了实体表示。多任务学习框架生成了与图钉嵌入、产品嵌入处于同一空间且兼容已有嵌入的单一搜索查询嵌入。通过消融实验，我们验证了各特征的价值，并展示了统一模型相比独立模型的优越性。最后，我们阐述了这些嵌入如何从检索到排序在Pinterest搜索栈中部署，并以低延迟扩展至每秒30万次请求的服务能力。本工作的实现代码已发布于https://github.com/pinterest/atg-research/tree/main/omnisearchsage。