user behavior analysis

Bilibili Tech

Jun 18, 2024 · Frontend Development

Design and Implementation of a Front-End Observability System for Business Monitoring

The article describes a unified front‑end observability platform that standardizes data‑point collection via a common SDK, automatically generates health and business dashboards, integrates real‑time monitoring and heatmaps, and has been adopted on 140 pages, delivering faster first‑screen loads, lower error and bounce rates, and higher conversion.

Dashboardfrontendobservability

0 likes · 22 min read

Design and Implementation of a Front-End Observability System for Business Monitoring

vivo Internet Technology

Feb 1, 2023 · Big Data

H5 Tracking Solution and Data Warehouse Design for User Behavior Analysis

The vivo Internet Big Data team presents a standardized, extensible H5 tracking solution that automates data collection via a JavaScript SDK for navigation, focus/blur, and visibility events, incorporates privacy safeguards, and feeds a multi‑layer data‑warehouse architecture with unified ID mapping and bitmap‑based retention modeling to support comprehensive user‑behavior dashboards and future advanced analyses.

Big DataData WarehouseH5 tracking

0 likes · 19 min read

H5 Tracking Solution and Data Warehouse Design for User Behavior Analysis

Bilibili Tech

Jan 10, 2023 · Big Data

Technical Evolution of Bilibili's PolarStar User Behavior Analysis Platform

Bilibili’s PolarStar platform evolved from Spark‑based batch jobs to a Flink‑driven real‑time pipeline and finally to a unified Iceberg‑on‑ClickHouse model, cutting query latency to seconds, saving thousands of CPU cores and hundreds of gigabytes of Redis memory while enabling complex, near‑real‑time user‑behavior analyses and scalable data‑import, rebalancing, and compression optimizations.

Big DataClickHouseData Warehouse

0 likes · 30 min read

Technical Evolution of Bilibili's PolarStar User Behavior Analysis Platform

Alimama Tech

Aug 4, 2021 · Big Data

Fast Attribution Engine (FAE): High‑Performance Distributed Computing for User Behavior and Advertising Attribution

FAE, Alibaba’s high‑performance distributed MPP engine, stores billions of user‑behavior events in a time‑ordered AFile model and uses stateless masters, importers, mergers and workers with Redis and MySQL metadata to deliver sub‑second, 10‑100× faster ad‑attribution queries across ad‑hoc, offline and near‑real‑time scenarios such as frequency, path and funnel analysis.

Ad AttributionBig DataFAE

0 likes · 11 min read

Fast Attribution Engine (FAE): High‑Performance Distributed Computing for User Behavior and Advertising Attribution

DataFunTalk

Aug 3, 2021 · Big Data

Fast Attribution Engine (FAE): A High‑Performance Distributed Computing Engine for User Behavior and Advertising Attribution

The article introduces Alibaba's Fast Attribution Engine (FAE), describing the technical challenges of user behavior and advertising attribution, its data model (AFile), system architecture, performance advantages over traditional OLAP solutions, and a range of application scenarios such as frequency analysis, crowd flow modeling, path, retention, funnel analysis, and visitor selection.

Big DataFAEMPP engine

0 likes · 13 min read

Fast Attribution Engine (FAE): A High‑Performance Distributed Computing Engine for User Behavior and Advertising Attribution

HelloTech

May 14, 2021 · Big Data

User Behavior Analysis System: Architecture, ClickHouse Cluster Deployment, and Analytical Techniques

The article describes a real‑time user behavior analysis platform built on a ClickHouse cluster, detailing its architecture, Hive‑to‑ClickHouse data ingestion with user‑ID routing, table designs for behavior and group data, and five analytical methods—event, funnel, path, retention, and attribution—leveraging shard‑level parallelism and custom functions for high efficiency.

Big DataClickHouseHive

0 likes · 20 min read

User Behavior Analysis System: Architecture, ClickHouse Cluster Deployment, and Analytical Techniques

58 Tech

Apr 16, 2021 · Artificial Intelligence

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

The article presents a comprehensive anti‑fraud framework that analyzes black‑market fraud characteristics, reviews conventional fraud‑mitigation methods, and proposes a multimodal graph‑neural‑network approach—leveraging device, behavior, and content similarity—to accurately identify fraudulent users on large‑scale internet platforms.

Graph Neural Networksanti-fraudfraud detection

0 likes · 18 min read

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

vivo Internet Technology

Mar 10, 2021 · Big Data

Path Analysis Model Design and Engineering Implementation for Internet Data Operations

The article details the design and engineering of a high‑performance path analysis model for internet data operations, explaining session handling, Sankey visualizations, adjacency‑table storage, multi‑granular session partitioning, Spark‑to‑ClickHouse pipelines, and optimizations that enable billion‑scale user‑path queries in about one second.

Big DataClickHouseData Modeling

0 likes · 21 min read

Path Analysis Model Design and Engineering Implementation for Internet Data Operations

Tencent Advertising Technology

Oct 29, 2020 · Artificial Intelligence

Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework

This article discusses a deep spatial-temporal tensor factorization framework for large-scale user visits understanding and forecasting, addressing challenges in advertising inventory prediction and demonstrating significant improvements over traditional methods.

advertising inventory predictiondata sciencedeep learning

0 likes · 9 min read

Large-Scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework

Beike Product & Technology

Sep 28, 2018 · Databases

Using ClickHouse for Large‑Scale User Behavior Analysis at Beike Zhaofang

This article details how Beike Zhaofang leveraged the ClickHouse columnar OLAP database for large‑scale user behavior analysis, covering its architecture, key features, performance benchmarks against other engines, data ingestion pipelines, custom UDFs for funnel and retention metrics, deployment setup, and future enhancements.

ClickHouseData EngineeringOLAP

0 likes · 13 min read

Using ClickHouse for Large‑Scale User Behavior Analysis at Beike Zhaofang

58 Tech

Dec 15, 2017 · Big Data

Design and Architecture of WMDA: A Comprehensive User Behavior Analysis Platform

The article details WMDA, a no‑code and manual‑code data collection platform for PC, mobile and app that supports real‑time and offline user behavior analysis, describing its functional model, behavior taxonomy, five‑layer architecture, tracking techniques, circle‑selection, data services, streaming and batch processing pipelines, and related technologies such as Storm, Spark, Druid and Roaring Bitmap.

Big DataDruidReal-time Streaming

0 likes · 18 min read

Design and Architecture of WMDA: A Comprehensive User Behavior Analysis Platform

iQIYI Technical Product Team

Aug 18, 2017 · Operations

iQiyi Video Buffering Analysis and Handling Experience

iQiyi monitors video buffering across millions of users, classifies anomalies into internal, server, operator, and user causes, uses a buffer perception system with clustering and SVM predictions, automates multi‑dimensional alerts, and resolves over 93% of non‑operator incidents within 15 minutes.

BufferingOperationsVideo Streaming

0 likes · 18 min read

iQiyi Video Buffering Analysis and Handling Experience

Baidu Intelligent Testing

Jun 16, 2016 · Fundamentals

User Behavior Analysis: Objectives, Implementation Methods, and Product Line Applications

The article explains the goals of user behavior analysis, details two implementation methods for single-step and multi-step path conversions using log preprocessing, adjacency matrices, and KMP pattern matching, and illustrates these techniques with a Baidu Doctor product line case study.

Data ModelingKMP algorithmconversion rate

0 likes · 6 min read

User Behavior Analysis: Objectives, Implementation Methods, and Product Line Applications