Paper Info
Title
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data
Abstract
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.
Year
DOI
Keywords
2019
10.1145/3292500.3330696
e-commerce image understanding, image classification
Field
DocType
ISSN
Computer science,Artificial intelligence,Contextual image classification,E-commerce,Machine learning
Conference
978-1-4503-6201-6
ISBN
Citations 
PageRank 
978-1-4503-6201-6
1
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Yina Tang1223.11
Fedor Borisyuk2363.58
Siddarth Malreddy311.38
Yixuan Li41709.46
Yiqun Liu51592136.51
sergey kirshner6191.67