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JD Tech Talk
JD Tech Talk
May 22, 2025 · Artificial Intelligence

From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection

The article recounts Xiaoting’s journey from a PhD research background to leading JD.com’s ad‑fraud detection, detailing how large language models, reinforcement learning, and model distillation were applied to identify hidden address codes, reduce false‑positive rates to 0.3%, and balance accuracy with real‑time performance in a high‑traffic e‑commerce environment.

AILLMModel Distillation
0 likes · 11 min read
From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection
vivo Internet Technology
vivo Internet Technology
Feb 15, 2023 · Information Security

Ad Traffic Anti‑Fraud: Algorithms, System Architecture, and Case Studies

The article explains how ad traffic fraud—ranging from simulated impressions to click farms—can be combated using a four‑layer risk‑control system that leverages unsupervised (DBSCAN, Isolation Forest) and supervised (Logistic Regression, Random Forest) algorithms, detailing data pipelines, model training, monitoring, and real‑world case studies.

Anomaly Detectionad fraudadvertising
0 likes · 15 min read
Ad Traffic Anti‑Fraud: Algorithms, System Architecture, and Case Studies
DataFunTalk
DataFunTalk
Jul 23, 2021 · Artificial Intelligence

Ad Fraud Detection and Risk Control Practices at Alibaba Mama

This article explains Alibaba Mama's ad fraud risk control workflow, defines invalid traffic types, describes perception, insight, and disposal mechanisms, and outlines the AI‑driven detection models, evaluation metrics, and future research directions for large‑scale advertising security.

AlibabaAnomaly Detectionad fraud
0 likes · 14 min read
Ad Fraud Detection and Risk Control Practices at Alibaba Mama