User Clustering Techniques in Tencent KanDian: From Traditional Algorithms to N‑gram and action2vec

This article explains how Tencent KanDian analyzes user behavior by introducing the product, describing common clustering scenarios, reviewing traditional unsupervised methods, and detailing advanced path‑based approaches such as N‑gram and action2vec, while discussing their advantages, limitations, and practical applications.

AIN-gramTencent

0 likes · 12 min read

User Clustering Techniques in Tencent KanDian: From Traditional Algorithms to N‑gram and action2vec

DataFunTalk

Nov 8, 2021 · Artificial Intelligence

User Behavior Clustering in Tencent Kankan: From Traditional Unsupervised Methods to N‑gram and action2vec

This article introduces Tencent Kankan's product landscape and explores various user clustering techniques—including classic unsupervised algorithms, N‑gram based sequence clustering, and deep‑learning driven action2vec—detailing their implementation steps, advantages, limitations, and practical insights for product optimization.

N-gramTencentaction2vec

0 likes · 12 min read

User Behavior Clustering in Tencent Kankan: From Traditional Unsupervised Methods to N‑gram and action2vec

WeChat Backend Team

Jan 18, 2018 · Information Security

How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework

This article explains how WeChat’s security team builds a scalable anomaly‑detection framework that partitions billions of user accounts, weights suspicious attributes, computes similarity graphs, and leverages Spark optimizations and graph‑partitioning techniques to efficiently identify malicious user clusters.

Large-Scale GraphSpark optimizationanomaly detection

0 likes · 18 min read

How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework