E-commerce search engines comprise a retrieval phase and a ranking phase, where the first one returns a candidate product set given user queries. Recently, vision-language pre-training, combining textual information with visual clues, has been popular in the application of retrieval tasks. In this paper, we propose a novel V+L pre-training method to solve the retrieval problem in Taobao Search. We design a visual pre-training task based on contrastive learning, outperforming common regression-based visual pre-training tasks. In addition, we adopt two negative sampling schemes, tailored for the large-scale retrieval task. Besides, we introduce the details of the online deployment of our proposed method in real-world situations. Extensive offline/online experiments demonstrate the superior performance of our method on the retrieval task. Our proposed method is employed as one retrieval channel of Taobao Search and serves hundreds of millions of users in real time.

翻译：电子商务搜索引擎由检索阶段和排序阶段组成，其中检索阶段根据用户查询返回候选商品集。近年来，融合文本信息与视觉线索的视觉语言预训练技术在检索任务中备受关注。本文提出一种新颖的视觉语言预训练方法，用于解决淘宝搜索中的检索问题。我们设计了基于对比学习的视觉预训练任务，其性能优于常规的基于回归的视觉预训练任务。此外，我们针对大规模检索任务采用两种负采样方案，并详细介绍了所提方法在真实场景中的在线部署细节。大量离线和在线实验表明，所提方法在检索任务上具有优越性能。该方法已作为淘宝搜索的检索通道之一，实时服务于数亿用户。