Prioritised Moderation for Online Advertising

Abstract

Online advertisement industry aims to build a preference for a product over its competitors by making consumers aware of the product at internet scale. However, the ads that violate the applicable laws and location specific regulations can have serious business impact with legal implications. At the same time, customers are at risk of getting exposed to egregious ads resulting in a bad user experience. Due to the limited and costly human bandwidth, moderating ads at the industry scale is a challenging task. Typically at Amazon Advertising, we deal with ad moderation workflows where the ad distributions are skewed by non defective ads. It is desirable to increase the review time that the human moderators spend on moderating genuine defective ads. Hence prioritisation of deemed defective ads for human moderation is crucial for the effective utilisation of human bandwidth in the ad moderation workflow. To incorporate the business knowledge and to better deal with the possible overlaps between the policies, we formulate this as a policy gradient ranking algorithm with custom scalar rewards. Our extensive experiments demonstrate that these techniques show a substantial gain in number of defective ads caught against various tabular classification algorithms, resulting in effective utilisation of human moderation bandwidth.