Product reviews have become a crucial part of the customer shopping journey, as they provide valuable feedback and insights from other customers. Amazon, with its vast selection of products, has become a reliable source of online reviews. However, it is important to ensure that these reviews adhere to Amazon’s Community Guidelines. To achieve this, Amazon employs content moderation automation using machine learning (ML) models.
One aspect of content moderation is image detection, as images often have a more immediate impact on customers than text. Amazon uses its own ML models, combined with human review, to detect images that violate guidelines. By automating decisions on a significant portion of the images, Amazon has improved accuracy and reduced the reliance on human moderators. This approach has also improved the well-being of human moderators and resulted in cost savings.
To further enhance its content moderation system, Amazon has migrated some of its self-hosted ML models to the Amazon Rekognition Content Moderation API. This API offers pre-trained models for image and video moderation, making it easier for businesses to detect inappropriate or unwanted content without requiring ML expertise. By leveraging the accuracy and comprehensiveness of the Rekognition API, Amazon has been able to automate more decisions, simplify its system architecture, reduce costs, and improve operational efficiency.
In conclusion, migrating to the Amazon Rekognition Moderation API for content moderation offers significant benefits for businesses. It allows for quick and accurate moderation of large volumes of product reviews, enhances the customer experience, reduces costs, and saves time and resources. The flexibility of the API also allows businesses to customize moderation rules to fit their specific needs.