基于弱监督搜索数据的大规模电子商务图像分类

Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey Kirshner
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引用次数: 16

摘要

本文提出了一种部署在大型商业搜索引擎中的图像识别系统,我们称之为MSURU。它被设计用来处理每天上传到Facebook Marketplace的产品图片。社交商务在Facebook中是一个不断发展的领域,理解产品内容的可视化表示对于Marketplace上的搜索和推荐应用程序非常重要。在本文中,我们介绍了使用弱监督搜索日志数据开发高效大规模图像分类器的技术。我们对提出的技术进行了广泛的评估,解释了开发大规模分类系统的实践经验,并讨论了我们面临的挑战。我们的系统MSURU在电子商务领域的表现比Facebook[23]开发的当前最先进的系统高出16%。MSURU投入生产后,在搜索成功率和Facebook Marketplace活跃互动方面有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data
In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace. In this paper, we present techniques we used to develop efficient large-scale image classifiers using weakly supervised search log data. We perform extensive evaluation of presented techniques, explain practical experience of developing large-scale classification systems and discuss challenges we faced. Our system, MSURU out-performed current state of the art system developed at Facebook [23] by 16% in e-commerce domain. MSURU is deployed to production with significant improvements in search success rate and active interactions on Facebook Marketplace.
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