Auto Content Moderation in C2C e-Commerce

Authors: 

Shunya Ueta, Suganprabu Nagaraja, and Mizuki Sango, Mercari, inc.

Abstract: 

Consumer-to-consumer (C2C) e-Commerce is a large and growing industry with millions of monthly active users. In this paper, we propose auto content moderation for C2C e-Commerce to moderate items using Machine Learning (ML). We will also discuss practical knowledge gained from our auto content moderation system. The system has been deployed to production at Mercari since late 2017 and has significantly reduced the operation cost in detecting items violating our policies. This system has increased coverage by 554.8 % over a rule-based approach.

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BibTeX
@inproceedings {256642,
author = {Shunya Ueta and Suganprabu Nagaraja and Mizuki Sango},
title = {Auto Content Moderation in C2C e-Commerce},
booktitle = {2020 {USENIX} Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/ueta},
publisher = {{USENIX} Association},
month = jul,
}

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