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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 withvisual clues, has been popular in the application of retrieval tasks. In thispaper, we propose a novel V+L pre-training method to solve the retrievalproblem in Taobao Search. We design a visual pre-training task based oncontrastive learning, outperforming common regression-based visual pre-trainingtasks. In addition, we adopt two negative sampling schemes, tailored for thelarge-scale retrieval task. Besides, we introduce the details of the onlinedeployment of our proposed method in real-world situations. Extensiveoffline/online experiments demonstrate the superior performance of our methodon the retrieval task. Our proposed method is employed as one retrieval channelof Taobao Search and serves hundreds of millions of users in real time.

Delving into E-Commerce Product Retrieval with Vision-Language Pre-training