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DataFunTalk
DataFunTalk
Aug 9, 2020 · Operations

A Practical Framework for Online Driver Repositioning to Balance Supply and Demand in Ride‑Hailing Platforms

This article presents a three‑stage, data‑driven framework for online driver repositioning that generates candidate dispatch tasks, scores them using a marginal gain model, and selects optimal tasks via a minimum‑cost flow planning algorithm, demonstrating significant improvements in driver efficiency and experience through large‑scale A/B experiments.

AB testingdriver repositioningfleet management
0 likes · 9 min read
A Practical Framework for Online Driver Repositioning to Balance Supply and Demand in Ride‑Hailing Platforms
Didi Tech
Didi Tech
Jul 16, 2020 · Operations

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

The paper describes Didi’s online driver‑repositioning system that treats idle‑driver dispatch as a recommender problem, generating candidate destinations, scoring tasks with a marginal‑gain model, and selecting optimal assignments via a minimum‑cost‑flow optimizer, which in live A/B tests boosted driver efficiency, earnings, and satisfaction while reducing empty cruising.

AB testingdriver repositioningfleet management
0 likes · 11 min read
When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning
vivo Internet Technology
vivo Internet Technology
Jun 3, 2020 · Product Management

A Comprehensive Guide to AB Testing: Methodology and Implementation

This comprehensive guide explains AB testing fundamentals—from defining control and experimental groups and avoiding confounding factors, to calculating sample size, selecting ratio‑based metrics, tracking data, monitoring experiments, and analyzing statistical significance—providing a step‑by‑step methodology for data‑driven product optimization.

A/B Testing MethodologyAB testingData‑Driven Decision Making
0 likes · 14 min read
A Comprehensive Guide to AB Testing: Methodology and Implementation
DataFunTalk
DataFunTalk
May 31, 2020 · Big Data

Adaptive Grouping Method for Improving AB Test Allocation Uniformity in Didi's Experiment Platform

This article introduces Didi's adaptive grouping algorithm, which enhances the uniformity of user allocation in AB experiments by replacing traditional complete randomization with a single-pass method that balances observed metrics across groups, and demonstrates its effectiveness through large‑scale experimental results.

AB testingData-drivenDidi
0 likes · 11 min read
Adaptive Grouping Method for Improving AB Test Allocation Uniformity in Didi's Experiment Platform
Didi Tech
Didi Tech
May 28, 2020 · Artificial Intelligence

Adaptive Grouping Method for AB Testing in Didi’s Experiment Platform

Didi’s AI Lab introduces an Adaptive grouping algorithm for its Apollo AB‑testing platform that allocates users in a single pass using direct and indirect scores, achieving over 95 % balance probability and reducing group imbalance from 14 % (CR) and 2.7 % (RR) to under 0.8 %.

AB testingData-drivenadaptive grouping
0 likes · 11 min read
Adaptive Grouping Method for AB Testing in Didi’s Experiment Platform
Ctrip Technology
Ctrip Technology
May 14, 2020 · Backend Development

Improving Ctrip's AB Experiment Splitter: Design, Performance Optimization, and Backend Architecture

The article details Ctrip's challenges with multiple AB testing splitters, presents performance gains after migrating to a new splitter, and explains the comprehensive redesign covering overall architecture, interface consolidation, SDK slimming, and a custom distributed cache backend to achieve higher throughput and lower latency.

AB testingCtripbackend design
0 likes · 12 min read
Improving Ctrip's AB Experiment Splitter: Design, Performance Optimization, and Backend Architecture
Amap Tech
Amap Tech
May 8, 2020 · Product Management

How Amap’s Evaluation Team Measures Product Success: Roles, Methods, and Tools

This article explains the evolution, responsibilities, and evaluation methods of the product‑effect assessment team at Amap, covering offline testing, AB experiments, metric analysis, automated scoring models, and the tools that support a comprehensive product‑performance framework.

AB testingIndustry InsightsMetrics
0 likes · 13 min read
How Amap’s Evaluation Team Measures Product Success: Roles, Methods, and Tools
Alibaba Terminal Technology
Alibaba Terminal Technology
Apr 27, 2020 · Frontend Development

Designing a Scalable Frontend AB Testing Framework: From Config to Runtime

This article outlines a comprehensive, standardized front‑end AB testing architecture that separates experiment configuration and data chains, introduces a JSSDK with Core and Coupler packages, and explains traffic‑splitting models, data back‑flow, and extensibility across multiple front‑end DSLs.

AB testingFrontend ArchitectureJSSDK
0 likes · 16 min read
Designing a Scalable Frontend AB Testing Framework: From Config to Runtime
58 Tech
58 Tech
Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB testingAIDeep Learning
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution
Continuous Delivery 2.0
Continuous Delivery 2.0
Mar 30, 2020 · Operations

Dynamic Runtime Configuration Management at Facebook: Use Cases and Tooling

The article explains how Facebook manages dynamic runtime configuration for millions of services—covering feature gating, experiments, traffic control, topology balancing, monitoring, machine‑learning model updates, and internal behavior—using a suite of tools such as Configerator, Gatekeeper, Package Vessel, Sitevars, and MobileConfig.

AB testingcloud operationsconfiguration-management
0 likes · 8 min read
Dynamic Runtime Configuration Management at Facebook: Use Cases and Tooling
JD Retail Technology
JD Retail Technology
Mar 26, 2020 · Backend Development

Design and Architecture of an Algorithm Business Platform for Rapid Online Service Development

The article details the design principles, modular architecture, and engineering optimizations of a backend algorithm platform that uses APIs, micro‑services, and asynchronous processing to enable fast, reliable, and scalable online algorithm services, including recall, ranking, metadata, feature reporting, and A/B testing.

AB testingAlgorithm PlatformBackend Architecture
0 likes · 9 min read
Design and Architecture of an Algorithm Business Platform for Rapid Online Service Development
HomeTech
HomeTech
Mar 18, 2020 · Artificial Intelligence

Automobile Home Recommendation System Architecture and Ranking Models

This article presents a comprehensive overview of the Automobile Home recommendation system, detailing its objectives, architecture, various ranking models from LR to DeepFM, online learning mechanisms, service APIs, feature engineering pipelines, model training platforms, debugging tools, and future optimization directions.

AB testingAutoMLOnline Learning
0 likes · 18 min read
Automobile Home Recommendation System Architecture and Ranking Models
DataFunTalk
DataFunTalk
Feb 28, 2020 · Artificial Intelligence

Evolution of Autohome's Recommendation System Ranking Algorithms

The article details the five‑year evolution of Autohome's recommendation system, covering its overall architecture, the progression of ranking models from LR to DeepFM and online learning, feature engineering pipelines, ranking service APIs, AB testing practices, and future optimization directions.

AB testingAIOnline Learning
0 likes · 20 min read
Evolution of Autohome's Recommendation System Ranking Algorithms
Xianyu Technology
Xianyu Technology
Feb 27, 2020 · Artificial Intelligence

Data-Driven Simulation for User Activity Retention Prediction

By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.

AB testingCARTGBDT
0 likes · 9 min read
Data-Driven Simulation for User Activity Retention Prediction
Meituan Technology Team
Meituan Technology Team
Feb 6, 2020 · Artificial Intelligence

Building a One-Stop Machine Learning Platform: Meituan's Turing Platform

Meituan’s Turing platform consolidates the entire delivery‑order workflow—from massive data ingestion and feature generation to model training, evaluation, deployment, real‑time prediction, and AB testing—into a single, end‑to‑end system that evolved from a minimal MVP into a fully platformized solution, addressing speed, accuracy, and engineering‑algorithm decoupling while planning deeper deep‑learning integration.

AB testingDeep LearningMachine Learning Platform
0 likes · 16 min read
Building a One-Stop Machine Learning Platform: Meituan's Turing Platform
HomeTech
HomeTech
Jan 15, 2020 · Artificial Intelligence

Architecture and Components of an Intelligent Recommendation Platform

The article outlines a micro‑service based intelligent recommendation platform that supports over 40 scenarios, detailing its overall architecture, AB testing service, and the three core modules—index, recall, and filter—while also describing future plans for platform centralization and open development.

AB testingAIEngine Architecture
0 likes · 5 min read
Architecture and Components of an Intelligent Recommendation Platform
Youzan Coder
Youzan Coder
Dec 6, 2019 · Big Data

Improving SparkSQL Stability and Performance at Youzan: Thrift Server Enhancements, Metric Collection, and Lessons Learned

Youzan’s big‑data team boosted SparkSQL stability and performance by reinforcing the Thrift Server, implementing AB gray‑release testing, collecting real‑time metrics, adding an engine‑selection service, and completing a second migration that raised SparkSQL’s workload share to 91 %, while documenting key pitfalls and tuning lessons.

AB testingSparkSQLThrift Server
0 likes · 15 min read
Improving SparkSQL Stability and Performance at Youzan: Thrift Server Enhancements, Metric Collection, and Lessons Learned
Meituan Technology Team
Meituan Technology Team
Nov 28, 2019 · Backend Development

Wedge: Design and Implementation of an Advertising Experiment Configuration Platform

Wedge is a Meituan‑Dianping advertising experiment configuration platform that provides extensible, flow‑based A/B testing with version control, real‑time monitoring, and a user‑friendly UI, enabling algorithm, engineering, and business teams to rapidly iterate, audit, and roll back complex vertical and horizontal experiments.

AB testingAdvertisingBackend
0 likes · 12 min read
Wedge: Design and Implementation of an Advertising Experiment Configuration Platform
58 Tech
58 Tech
Nov 15, 2019 · Artificial Intelligence

From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform

This article presents a comprehensive case study of how 58.com built a personalized recommendation system for its large‑scale recruitment platform, covering business background, data challenges, user modeling, recall strategies, ranking pipelines, system architecture, experimental infrastructure, and future research directions.

AB testingfeature engineeringknowledge graph
0 likes · 18 min read
From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform
Ctrip Technology
Ctrip Technology
Sep 4, 2019 · Artificial Intelligence

Design and Implementation of Ctrip's User Precise Marketing System

This article details the design goals, architecture, core functionalities, and optimization strategies of Ctrip's user precise marketing system, which leverages RESTful integration, flexible rule-based and machine‑learning models, real‑time monitoring, and AB testing to improve traffic utilization and conversion rates.

AB testingCtripMarketing
0 likes · 11 min read
Design and Implementation of Ctrip's User Precise Marketing System
Xianyu Technology
Xianyu Technology
Jul 25, 2019 · Product Management

Design of a Gameplay System for User Engagement on Xianyu

The Xianyu team built a flexible gameplay engine that lets product define task‑based challenges, run bucket‑AB tests, and provide real‑time rewards, boosting user participation to over 70% and increasing activity by 30%, while enabling rapid iteration and future expansion across the app and other services.

AB testingProduct DesignXianyu
0 likes · 6 min read
Design of a Gameplay System for User Engagement on Xianyu
NetEase Media Technology Team
NetEase Media Technology Team
Jun 5, 2019 · Product Management

Mastering AB Testing: From Basics to Scalable Multi‑Layer Architecture

This article explains the fundamentals of AB testing, outlines the iterative workflow, shares best‑practice guidelines, compares single‑layer and multi‑layer experiment frameworks, and details the technical implementation—including SDK design, hashing algorithms, data denoising, and statistical evaluation methods.

AB testingBackendHashing
0 likes · 15 min read
Mastering AB Testing: From Basics to Scalable Multi‑Layer Architecture
58UXD
58UXD
May 29, 2019 · Product Management

How Data Analysis Drives User Growth: From AARRR Funnel to Practical Tools

This article explains fundamental data‑analysis methods, introduces the AARRR user‑growth model with key metrics for each stage, and presents practical tools such as user path analysis, funnel conversion, heatmaps, and A/B testing to help product teams make data‑driven decisions and continuously improve user experience.

AARRRAB testingUser experience
0 likes · 9 min read
How Data Analysis Drives User Growth: From AARRR Funnel to Practical Tools
Beike Product & Technology
Beike Product & Technology
Jan 10, 2019 · Backend Development

Design and Implementation of the AB Experiment Platform at Beike Zhaofang

The article details the design principles, layered traffic allocation model, architecture, data processing pipeline, and operational experience of the AB experiment platform used at Beike Zhaofang, highlighting its web, API, and storage layers, gray‑release capabilities, current limitations, and future improvements.

AB testingBackend ArchitectureExperiment Platform
0 likes · 15 min read
Design and Implementation of the AB Experiment Platform at Beike Zhaofang
58 Tech
58 Tech
Sep 7, 2018 · Artificial Intelligence

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

The article details how 58.com’s Cupid push control system leverages machine‑learning models, especially XGBoost‑based CTR prediction, to prioritize and filter billions of daily push notifications, improving click‑through rates, reducing user annoyance, and providing a scalable, data‑driven architecture for diverse business services.

AB testingCTR predictionSystem Architecture
0 likes · 13 min read
Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com
Baidu Intelligent Testing
Baidu Intelligent Testing
Jun 29, 2018 · Product Management

Baidu Product Evaluation Framework and Common Assessment Methods

This article outlines Baidu's comprehensive product evaluation framework, describing its multi‑layer assessment system, the combination of subjective and objective metrics, and a suite of common evaluation methods such as indicator analysis, AB testing, user feedback, behavior analysis, big‑data profiling, and competitor comparison.

AB testingBig DataMetrics
0 likes · 16 min read
Baidu Product Evaluation Framework and Common Assessment Methods
Hulu Beijing
Hulu Beijing
Jun 26, 2018 · Operations

How Hulu Optimizes Video QoS: Adaptive Bitrate Strategies and Real‑World Insights

This article explains Hulu's comprehensive approach to streaming quality optimization, covering the video system architecture, business model, the distinction between QoS and QoE, key performance metrics, adaptive bitrate algorithms, data‑driven workflows, offline validation, online A/B testing, and the measurable improvements achieved across multiple platforms.

AB testingData-drivenQoS
0 likes · 25 min read
How Hulu Optimizes Video QoS: Adaptive Bitrate Strategies and Real‑World Insights
dbaplus Community
dbaplus Community
Feb 8, 2018 · Artificial Intelligence

Unlocking Data Value: A Practical Guide to Bayesian Theorem and Its Applications

This article explains the fundamentals of Bayes' theorem, shows how to compute prior, likelihood, and posterior probabilities, demonstrates Bayesian A/B testing with Python code, introduces Bayesian networks for causal inference, and discusses the role of Bayesian methods in machine learning and data‑driven decision making.

AB testingBayesianStatistical Modeling
0 likes · 11 min read
Unlocking Data Value: A Practical Guide to Bayesian Theorem and Its Applications
360 Quality & Efficiency
360 Quality & Efficiency
Feb 5, 2018 · Artificial Intelligence

Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods

The article explains the core concepts of recommendation engines—user profiling and data classification—describes how large‑scale data processing tools are used to build models, and outlines common offline and A/B testing approaches for evaluating recommendation performance.

AB testingdata classificationmachine learning
0 likes · 4 min read
Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods
AntTech
AntTech
Dec 1, 2017 · Big Data

Insights and Paper Summaries from KDD 2017 Conference

The article provides a comprehensive overview of KDD 2017, including acceptance statistics, best paper awards, Ant Group's contributions, detailed discussions on AB testing, graph mining, and selected research papers across data mining, machine learning, and anomaly detection, offering valuable insights for practitioners and researchers.

AB testingBig DataKDD
0 likes · 30 min read
Insights and Paper Summaries from KDD 2017 Conference
Baidu Waimai Technology Team
Baidu Waimai Technology Team
Aug 3, 2017 · Artificial Intelligence

Model Testing and Evaluation Metrics for Strategy Projects in the AI Era

This article explains the challenges of testing machine‑learning models for strategy projects, outlines the overall testing workflow, describes key offline and online evaluation metrics such as AUC and AB‑testing, and summarizes best‑practice procedures for assessing model performance, user experience, and effect differences.

AB testingAIAUC
0 likes · 8 min read
Model Testing and Evaluation Metrics for Strategy Projects in the AI Era
Suning Design
Suning Design
Apr 26, 2017 · Product Management

What Tiny Design Changes Reveal About Purchase Intent and User Engagement

This article explains how subtle AB‑test variations—such as rounding prices or adjusting font size—can dramatically affect purchase intent and comment participation, backed by two real‑world case studies and practical takeaways for designers and product managers.

AB testingUX designconversion optimization
0 likes · 7 min read
What Tiny Design Changes Reveal About Purchase Intent and User Engagement
Architecture Digest
Architecture Digest
Apr 9, 2017 · Artificial Intelligence

Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting

The article details how Youku Tudou redesigned its video recommendation architecture, moving ranking from offline to online processing, outlining the comparative architecture, benefits, challenges, feature handling, offline evaluation methods, and weight‑fusion techniques that enabled a successful launch after two months of development.

AB testingAUC evaluationfeature engineering
0 likes · 7 min read
Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting
21CTO
21CTO
Mar 22, 2017 · Artificial Intelligence

How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking

The Youku Tudou data team overhauled its video recommendation system by moving ranking from offline to online, detailing architectural changes, advantages, challenges, feature handling, offline evaluation, and model weight fusion to improve scalability and user experience.

AB testingAISystem Architecture
0 likes · 7 min read
How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking
Ctrip Technology
Ctrip Technology
Feb 23, 2017 · Product Management

Applying AB Testing in Ctrip Flight Booking: Process, Data Flow, and Analysis

The article explains how Ctrip’s flight‑booking team uses AB testing—from definition and experimental design to data collection, traffic allocation, orthogonal experiments, and result analysis—to drive conversion‑rate and revenue improvements across multiple platforms.

AB testingconversion ratedata analysis
0 likes · 10 min read
Applying AB Testing in Ctrip Flight Booking: Process, Data Flow, and Analysis
21CTO
21CTO
Mar 12, 2016 · R&D Management

How Hybrid Cloud and Bottom‑Up Management Boosted Weibo’s Feed System

The article shares practical lessons from a year of backend R&D experiments at Weibo, describing how a bottom‑up hybrid‑cloud solution, cross‑team collaboration, and systematic team‑building transformed the Feed service’s scalability, cost efficiency, and alignment with business goals.

AB testingR&D managementbackend-development
0 likes · 11 min read
How Hybrid Cloud and Bottom‑Up Management Boosted Weibo’s Feed System
Ctrip Technology
Ctrip Technology
Dec 4, 2015 · Operations

Key Takeaways from Ctrip’s Second Investment Summit Technical Session

The eight‑hour technical session at Ctrip’s second investment summit in Shanghai featured a series of expert talks covering infrastructure scaling, automation frameworks, call‑center efficiency, secure development lifecycles, pricing engine architecture, high‑availability engineering, AB testing strategies, and mobile app componentization, providing valuable insights for large‑scale internet companies.

AB testingCtripTech Conference
0 likes · 10 min read
Key Takeaways from Ctrip’s Second Investment Summit Technical Session