JD.com's AI-Powered UGC Content Management System for E-Commerce Reviews

This article details JD.com's comprehensive AI-powered system for managing user-generated content (UGC) in e-commerce reviews. The system addresses four key challenges: identifying prohibited content, folding low-quality reviews, implementing differentiated incentive mechanisms, and mining user opinions for traffic optimization.

The platform uses knowledge graphs and deep learning to detect prohibited content including abusive language, political content, regional discrimination, pornography, gambling, recruitment ads, and scams. These models, deployed on JDOS container management, process millions of reviews daily to filter harmful content.

For low-quality content, the system identifies various problematic review types including gibberish, filler text, and joke reviews that can overwhelm review sections. Machine learning models fold these meaningless reviews to improve content quality and help users find valuable information more easily.

The platform implements differentiated incentive mechanisms using semantic value scoring. Based on product attributes and knowledge graph analysis, reviews are scored for their semantic value, with higher-quality original content receiving multiplied rewards in JD beans. Strict anti-fraud measures ensure only genuine contributions are rewarded.

Opinion mining extracts valuable user perspectives from review text, generating semantic tags for 90% of SKUs. These algorithmically generated tags help users quickly access relevant information and guide purchasing decisions. The system plans to use semantic dimensions for ranking to better showcase reviews aligned with user interests.

The Euler platform, developed by JD's technical architecture team, serves as the knowledge computing foundation, managing billions of knowledge entities and rules across products, brands, attributes, and public opinion. It actively manages problematic products and low-quality reviews to maintain ecosystem health.