Same-style products retrieval plays an important role in e-commerce
platforms, aiming to identify the same products which may have different text
descriptions or images. It can be used for similar products retrieval from
different suppliers or duplicate products detection of one supplier. Common
methods use the image as the detected object, but they only consider the visual
features and overlook the attribute information contained in the textual
descriptions, and perform weakly for products in image less important
industries like machinery, hardware tools and electronic component, even if an
additional text matching module is added. In this paper, we propose a unified
vision-language modeling method for e-commerce same-style products retrieval,
which is designed to represent one product with its textual descriptions and
visual contents. It contains one sampling skill to collect positive pairs from
user click log with category and relevance constrained, and a novel contrastive
loss unit to model the image, text, and image+text representations into one
joint embedding space. It is capable of cross-modal product-to-product
retrieval, as well as style transfer and user-interactive search. Offline
evaluations on annotated data demonstrate its superior retrieval performance,
and online testings show it can attract more clicks and conversions. Moreover,
this model has already been deployed online for similar products retrieval in
this http URL, the largest B2B e-commerce platform in the world.

本文提出一种统一的视觉 - 语言建模方法，用于电子商务同款产品检索，包括取样和对比学习，可进行跨模态产品检索和用户交互搜索，离线和在线测试都表明其卓越的检索性能和吸引更多点击和转化的能力，并已在全球最大的 B2B 电子商务平台中部署用于同款产品检索。