FAIR: Fraud Aware Impression Regulation System in Large-Scale Real-Time E-Commerce Search Platform
| Autor: | Junshuai Song, Zehong Hu, Jun Gao, Zhao Li, Shichang Hu, Long Zhang, Shasha Ruan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology E-commerce Computer security computer.software_genre Blacklist Impression 020204 information systems 0202 electrical engineering electronic engineering information engineering Blacklisting ComputingMilieux_COMPUTERSANDSOCIETY Reinforcement learning Revenue 020201 artificial intelligence & image processing Function (engineering) business computer media_common Reputation |
| Zdroj: | ICDE |
| Popis: | Fraud sellers in e-commerce usually promote their products via fake transactions. Such behaviors damage the reputation of the e-commerce platform and jeopardize the business environment in the platform. The search engine of existing e-commerce platforms mainly focuses on generating transactions by matching users' queries and sellers' products. The most common method to defense fraud sellers is to set up a blacklist based on fraud detection and manual investigation, and then punish those sellers in the list, which is inefficient and can only cover a small fraction of potential fraud sellers. In this paper, we propose the first fraud aware impression regulation system (FAIR) which is data-driven and can work in large-scale e-commerce platforms. Its main function is to actively regulate the impressions received by all potential fraud sellers in a real-time fashion. It utilizes the reinforcement learning architecture to dynamically adjust the impression regulation strategy under different reward settings, which can not only promote the impression regulation effects, but also improve the revenue of the platform simultaneously. We deploy FAIR on the Taobao platform of Alibaba, one of the world's largest e-commerce search platform, and perform an A/B test for two weeks. The results show that FAIR can effectively reduce the fraud impressions and improve the overall platform revenue at the same time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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