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SuanNi
SuanNi
Mar 31, 2026 · Industry Insights

What Anthropic’s New Economic Index Reveals About Claude’s Growing User Base

Anthropic’s March 2026 Economic Index analyzes over two million Claude.ai and API conversations, showing how usage is spreading from high‑skill professional tasks to everyday activities, how model choice varies by task value, and how longer‑time users achieve higher success rates, highlighting emerging AI adoption trends and skill gaps.

AI productivityAnthropicClaude
0 likes · 15 min read
What Anthropic’s New Economic Index Reveals About Claude’s Growing User Base
Amap Tech
Amap Tech
Dec 3, 2025 · Artificial Intelligence

How Gaode’s G‑Action Uses Generative AI to Predict Users’ Next Move

Gaode’s G‑Action framework combines large‑language‑model pre‑training with fine‑tuned generative recommendation to predict a user’s immediate action and destination, transforming static map services into a dynamic, context‑aware experience and delivering measurable gains in click‑through and engagement metrics.

AIMap Serviceslarge language model
0 likes · 15 min read
How Gaode’s G‑Action Uses Generative AI to Predict Users’ Next Move
DataFunTalk
DataFunTalk
Sep 22, 2025 · Artificial Intelligence

What 700 Million Users Really Do with ChatGPT: Surprising Insights from OpenAI’s Study

A recent OpenAI‑partnered research paper analyzing billions of ChatGPT interactions reveals that 70% of conversations are non‑work related, three core use cases cover 80% of demand, asking for advice now outpaces doing tasks, and the user base is becoming younger, more gender‑balanced, and globally diverse.

AI usageChatGPTDigital Transformation
0 likes · 12 min read
What 700 Million Users Really Do with ChatGPT: Surprising Insights from OpenAI’s Study
Python Programming Learning Circle
Python Programming Learning Circle
Sep 8, 2025 · Big Data

Unlocking E‑Commerce Insights: How Python & SQL Reveal User Behavior and Boost Sales

This article analyzes a JD e‑commerce dataset using Python and MySQL to calculate key metrics such as PV, UV, conversion rates, attrition, daily activity, hourly trends, user‑behavior funnels, purchase intervals, retention rates, product sales, and RFM segmentation, and then offers data‑driven recommendations to improve traffic, conversion, and user loyalty.

PythonRFMSQL
0 likes · 37 min read
Unlocking E‑Commerce Insights: How Python & SQL Reveal User Behavior and Boost Sales
21CTO
21CTO
Apr 22, 2025 · Artificial Intelligence

Does Politeness to ChatGPT Really Cost Millions in Electricity?

Sam Altman revealed that excessive politeness toward ChatGPT, such as saying “please” and “thank you,” can drive up server workload and electricity use, potentially costing OpenAI tens of millions of dollars, while highlighting broader concerns about AI energy consumption and user behavior.

ChatGPTEnergy ConsumptionSustainability
0 likes · 5 min read
Does Politeness to ChatGPT Really Cost Millions in Electricity?
Cognitive Technology Team
Cognitive Technology Team
Mar 31, 2025 · Artificial Intelligence

Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling

The article explains how Douyin's recommendation system uses machine‑learning and deep‑learning models to predict user actions, assign value weights, and dynamically adjust scores, highlighting both its efficiency in large‑scale content distribution and its inherent limitations compared to human understanding.

AIDeep Learningrecommendation system
0 likes · 7 min read
Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling
FunTester
FunTester
Mar 16, 2025 · Fundamentals

Supermarket Checkout – Round Four: Comprehensive Real-World Simulation and Core Performance Test Design Principles

The fourth round of supermarket checkout simulation reveals hidden complexities such as receipt‑paper replacement, cash‑change shortages, and cart blockages, leading to a set of core performance‑testing design principles that emphasize realistic user behavior modeling, data volume, environment fidelity, diversity, and iterative feedback.

Data Volumeenvironment configurationsimulation
0 likes · 6 min read
Supermarket Checkout – Round Four: Comprehensive Real-World Simulation and Core Performance Test Design Principles
DataFunSummit
DataFunSummit
Jul 25, 2024 · Artificial Intelligence

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control

This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.

graph neural networkslarge language modelsmachine learning
0 likes · 14 min read
LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control
vivo Internet Technology
vivo Internet Technology
Apr 17, 2024 · Big Data

Retention Analysis Model Practice Based on ClickHouse

The article explains retention analysis models, their importance for user loyalty, outlines offline Hive architecture, then shows how ClickHouse’s retention() function and columnar storage dramatically speed up multi‑day retention calculations, providing SQL examples and practical guidance for product analytics.

ClickHouseHiveRetention Analysis
0 likes · 17 min read
Retention Analysis Model Practice Based on ClickHouse
Python Programming Learning Circle
Python Programming Learning Circle
Nov 22, 2023 · Big Data

E‑commerce User Behavior Analysis and KPI Modeling with Python and SQL

This study analyzes JD e‑commerce operational data from February to April 2018, employing Python and SQL to compute key metrics such as PV, UV, conversion rates, attrition, purchase frequency, time‑based behavior, funnel analysis, retention, product sales, and RFM segmentation, and provides actionable recommendations for improving user engagement and sales performance.

MetricsRFMSQL
0 likes · 30 min read
E‑commerce User Behavior Analysis and KPI Modeling with Python and SQL
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 31, 2023 · Frontend Development

User Behavior Recording Techniques: Video, Screenshot, and DOM Snapshot (rrweb) Comparison and Implementation

This article examines various user behavior recording methods—including WebRTC video capture, canvas-based screenshot recording, and DOM snapshot recording with rrweb—detailing their technical implementations, advantages, limitations, and suitable application scenarios for product analysis, debugging, and automated testing.

VueWebRTCfrontend
0 likes · 28 min read
User Behavior Recording Techniques: Video, Screenshot, and DOM Snapshot (rrweb) Comparison and Implementation
Test Development Learning Exchange
Test Development Learning Exchange
Oct 28, 2023 · Databases

How Data Analysis Improves User Experience: Methods and Practical SQL Code Examples

This article explains ten data‑analysis techniques for enhancing user experience—such as behavior tracking, A/B testing, sentiment analysis, and personalization—and provides concrete SQL code snippets that illustrate how to import, query, filter, sort, aggregate, join, update, delete, and back up data in relational databases.

A/B testingSQLUser experience
0 likes · 8 min read
How Data Analysis Improves User Experience: Methods and Practical SQL Code Examples
DataFunTalk
DataFunTalk
Nov 11, 2022 · Product Management

Data Tracking (埋点) Application Scenarios, Workflow, and the Seven‑Word Guideline

This article explains the concept of data tracking (埋点), outlines its key application scenarios such as exposure, click, and page‑event tracking, describes the end‑to‑end workflow from requirement gathering to deployment and post‑analysis, and summarizes the practical “seven‑word” checklist for successful implementation.

Data Trackingdata collectionproduct analytics
0 likes · 12 min read
Data Tracking (埋点) Application Scenarios, Workflow, and the Seven‑Word Guideline
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Sep 20, 2022 · Product Management

Repurchase Strategy in Game Item Recommendation: Scenarios, Challenges, and Implementation

The article examines repurchase strategies for game item recommendations, analyzing various recommendation scenarios, their specific challenges, item classification based on purchase density and repurchase rates, and practical guidelines for applying the strategy across permanent shop, limited‑time gift packs, and refreshable recommendations.

game itemsproduct-managementrecommendation
0 likes · 11 min read
Repurchase Strategy in Game Item Recommendation: Scenarios, Challenges, and Implementation
Python Programming Learning Circle
Python Programming Learning Circle
Aug 17, 2022 · Big Data

Game Industry User Data Analysis: Registration Distribution, Payment Metrics, and Consumption Patterns

This article presents a comprehensive Python-based analysis of a large game dataset (2.29 million records, 109 fields), covering user registration trends, payment rates, ARPU/ARPPU calculations, level‑based spending behavior, and consumption patterns of resources and acceleration items, with visualizations and actionable conclusions.

Big DataGame AnalyticsPython
0 likes · 11 min read
Game Industry User Data Analysis: Registration Distribution, Payment Metrics, and Consumption Patterns
DataFunTalk
DataFunTalk
Jul 9, 2022 · Artificial Intelligence

User Behavior Sequence Based Transaction Anti‑Fraud Detection

This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.

Deep LearningEmbeddingfraud detection
0 likes · 12 min read
User Behavior Sequence Based Transaction Anti‑Fraud Detection
21CTO
21CTO
Mar 30, 2022 · Big Data

What Drives Taobao App Users? Insights from AARRR and RFM Analyses

This article analyzes 2 million Taobao app user‑behavior records using AARRR funnel metrics and RFM segmentation, revealing daily and hourly usage patterns, conversion bottlenecks, product‑search mismatches, and offering data‑driven marketing recommendations to boost retention and sales.

AARRRBig DataRFM
0 likes · 25 min read
What Drives Taobao App Users? Insights from AARRR and RFM Analyses
DevOps
DevOps
Dec 30, 2021 · Frontend Development

Rethinking Frontend Testing: Move Away from Implementation‑Detail Focus to Real‑User Behavior

This article explains why front‑end tests that concentrate on implementation details become fragile and time‑consuming, and argues for writing tests that mimic real user interactions using Testing Library, while recognizing that many small functions lack independent business value and should be tested at the UI level instead of as isolated unit tests.

React HooksTest StrategyUnit Tests
0 likes · 9 min read
Rethinking Frontend Testing: Move Away from Implementation‑Detail Focus to Real‑User Behavior
政采云技术
政采云技术
Dec 16, 2021 · Big Data

What Is Event Tracking (埋点) and Its Implementation in a Data Analysis System

This article explains the concept of event tracking (埋点), its importance for capturing user behavior, outlines the four‑module architecture of a tracking system, compares code‑based, visual and full tracking methods, describes data models, storage, management, and presents a practical case study with analysis techniques.

AnalyticsBackendBig Data
0 likes · 15 min read
What Is Event Tracking (埋点) and Its Implementation in a Data Analysis System
58UXD
58UXD
Dec 6, 2021 · Fundamentals

How to Decode User Behavior Data to Uncover Hidden Design Insights

This article explains how designers can shift from macro metrics like DAU to analyzing raw user interaction data, infer user psychology, reconstruct real usage scenarios, and identify optimization opportunities through concrete examples and step‑by‑step methods.

UX designbehavioral analyticsdata analysis
0 likes · 8 min read
How to Decode User Behavior Data to Uncover Hidden Design Insights
Alimama Tech
Alimama Tech
Aug 25, 2021 · Artificial Intelligence

Advertising Creative Optimization Using Hybrid Bandit Models

The article describes Alibaba Moments’ advertising creative optimization platform, which uses hybrid bandit models that combine visual‑aware ranking priors with exploration‑exploitation algorithms such as Thompson Sampling and LinUCB to dynamically select whole creatives or individual elements, improving click‑through rates and mitigating cold‑start challenges.

Algorithmic Optimizationadvertising creativesbandit models
0 likes · 14 min read
Advertising Creative Optimization Using Hybrid Bandit Models
Didi Tech
Didi Tech
Feb 4, 2021 · Mobile Development

DoKit One‑Machine‑Multi‑Control: Principles, Usage Scenarios and Open‑Source Plans

DoKit’s one‑machine‑multi‑control lets an Android host device manage slave devices over a LAN without extra permissions or code intrusion, dramatically streamlining functional regression and compatibility testing while supporting user‑behavior recording, and is slated for open‑source release with future extensions to Flutter and Web.

AndroidDoKitcross‑platform
0 likes · 8 min read
DoKit One‑Machine‑Multi‑Control: Principles, Usage Scenarios and Open‑Source Plans
DataFunTalk
DataFunTalk
Jan 1, 2021 · Artificial Intelligence

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.

Knowledge GraphRecommendation Systemsartificial intelligence
0 likes · 12 min read
Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems
DataFunTalk
DataFunTalk
Jul 19, 2020 · Product Management

Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms

This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.

Recommendation Systemsdata analysismatching algorithms
0 likes · 30 min read
Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms
政采云技术
政采云技术
May 17, 2020 · Frontend Development

Building a User Behavior Data Collection and Analysis System (Hunyi) – Frontend Team Experience

This article describes how the frontend team designed and implemented a comprehensive user behavior data collection and analysis platform, covering its business value, overall architecture, SDK-based data gathering, event interception, processing pipelines, analytics dashboards, and practical insights for product and operations teams.

AnalyticsSDKdata collection
0 likes · 15 min read
Building a User Behavior Data Collection and Analysis System (Hunyi) – Frontend Team Experience
JD.com Experience Design Center
JD.com Experience Design Center
May 9, 2020 · Fundamentals

10 Essential E‑Commerce Metrics Every Analyst Should Master

This article explains the purpose and types of data metrics, outlines a logical framework for analyzing e‑commerce performance from traffic to behavior to transaction, and details ten key metrics—including GMV, conversion rate, UV value, click‑through and exposure rates—along with practical interpretation tips.

AnalyticsGMVconversion rate
0 likes · 12 min read
10 Essential E‑Commerce Metrics Every Analyst Should Master
58 Tech
58 Tech
Feb 10, 2020 · Big Data

Construction and Practice of a Site-wide User Behavior Data Warehouse at 58.com

This article systematically describes the challenges, design principles, modeling methods, layered architecture, implementation steps, and standards used in building a comprehensive user behavior data warehouse for 58.com, highlighting practical experiences and future improvement directions.

Big DataData QualityData Warehouse
0 likes · 11 min read
Construction and Practice of a Site-wide User Behavior Data Warehouse at 58.com
FangDuoduo UEDC
FangDuoduo UEDC
Dec 10, 2019 · Fundamentals

Why the QWERTY Keyboard Still Dominates: History, Design Tricks, and Human Habits

The article explores the historical origins of the QWERTY layout, how its design intentionally slowed typing to prevent mechanical jams, why alternative ergonomic keyboards failed to replace it, and the psychological habit‑formation factors that keep users locked into this seemingly inefficient standard.

QWERTYergonomicshabit formation
0 likes · 8 min read
Why the QWERTY Keyboard Still Dominates: History, Design Tricks, and Human Habits
Xianyu Technology
Xianyu Technology
Nov 7, 2019 · Big Data

Sequence Pattern Mining for User Behavior Analysis in Xianyu

By applying sequence pattern mining and unsupervised clustering to Xianyu’s massive event logs, the study abstracts high‑level user behaviors, discovers frequent subsequences, uncovers unknown fraudulent account patterns, expands known fraud cohorts with 99 % precision, and enables richer analyses such as PCA‑based cross‑group comparisons.

Big Dataclusteringdata mining
0 likes · 8 min read
Sequence Pattern Mining for User Behavior Analysis in Xianyu
DataFunTalk
DataFunTalk
Jul 1, 2019 · Artificial Intelligence

Data-Driven Foundations for Building Recommendation Systems

The article explains how data serves as a critical asset for recommendation systems, outlining the necessary steps from understanding business problems and data dimensions to collection, cleaning, integration, and analysis, while distinguishing explicit and implicit user feedback and emphasizing data quality, timeliness, and relevance.

Data QualityETLRecommendation Systems
0 likes · 11 min read
Data-Driven Foundations for Building Recommendation Systems
Ctrip Technology
Ctrip Technology
Jun 4, 2019 · Artificial Intelligence

Ctrip Search Recommendation System Architecture and Evolution

This article presents an overview of Ctrip's travel recommendation system, detailing its architecture, user‑behavior analysis, product catalog handling, various recall strategies, ranking methods—including machine‑learning models like XGBoost—and future directions toward deeper AI and NLP integration.

Ctripcollaborative filteringranking
0 likes · 9 min read
Ctrip Search Recommendation System Architecture and Evolution
网易UEDC
网易UEDC
Mar 11, 2019 · Product Management

Unlocking User Retention: How to Identify and Leverage the Aha Moment

This article explains why products lose users, defines the Aha Moment as the pivotal instant of value discovery, and provides a step‑by‑step framework—including hypothesis formulation, cohort analysis, and optimal action thresholds—to capture that moment and boost retention through data‑driven growth experiments.

Aha Momentcohort analysisuser behavior
0 likes · 13 min read
Unlocking User Retention: How to Identify and Leverage the Aha Moment
58UXD
58UXD
Nov 2, 2018 · Product Management

How Real-World User Motives Shape Modern Map Design

This article explores how everyday location migrations reveal deeper user motivations, detailing Baidu Maps' evolution from precise navigation to multi‑service 4K, 3D, and AOI maps, while examining mixed travel modes, environmental variables, and design strategies for tool‑type products.

Location ServicesMixed Travel Modesmap design
0 likes · 10 min read
How Real-World User Motives Shape Modern Map Design
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 10, 2018 · Big Data

Data-Driven Entertainment: iQIYI’s Big Data Platform and AI Applications

iQIYI’s unified “Tongtian Tower” big‑data platform integrates analytics, AI and open APIs to turn viewer behavior and public sentiment into market insights, personalized recommendations, smart casting and churn‑prediction tools, embedding a data‑driven culture that fuels its rapid subscriber growth and revenue surge.

AIBig DataData Platform
0 likes · 12 min read
Data-Driven Entertainment: iQIYI’s Big Data Platform and AI Applications
Meitu Technology
Meitu Technology
Jul 17, 2018 · Artificial Intelligence

Video Clustering Techniques for Personalized Recommendation in Meipai

Meipai’s personalized recommendation system leverages massive user‑behavior data to build behavior‑driven video clusters—evolving from TopicModel through Item2vec and Keyword Propagation to a DSSM deep model—boosting ranking AUC, enhancing UI diversity, similar‑video search, niche discovery, and feature engineering.

DSSMItem2Veckeyword propagation
0 likes · 22 min read
Video Clustering Techniques for Personalized Recommendation in Meipai
Meituan Technology Team
Meituan Technology Team
Jul 5, 2018 · Big Data

Meituan Dianping User Action System (UAS): Architecture and Implementation for Real-time User Behavior Processing

Meituan‑Dianping’s User Action System unifies disparate user‑behavior events with a 5W1H format, ingests them via a proprietary MAPI channel into Kafka, processes them in real‑time using Storm and a Lambda batch‑speed architecture, and delivers millisecond‑level responses for billions of daily events while offering flexible, modular query and storage options.

KafkaLambda architectureStorm
0 likes · 17 min read
Meituan Dianping User Action System (UAS): Architecture and Implementation for Real-time User Behavior Processing
21CTO
21CTO
Dec 17, 2017 · Artificial Intelligence

How Collaborative Filtering Turns User Behavior into Smart Recommendations

This article explains the fundamentals of collaborative filtering, detailing explicit and implicit user feedback, power‑law behavior patterns, neighborhood‑based and latent‑factor recommendation algorithms, and how they are applied in e‑commerce and social platforms.

AIRecommendation Systemscollaborative filtering
0 likes · 8 min read
How Collaborative Filtering Turns User Behavior into Smart Recommendations
Architecture Digest
Architecture Digest
Dec 17, 2017 · Artificial Intelligence

Introduction to User Behavior and Collaborative Filtering in Recommendation Systems

This article explains user behavior concepts and feedback types, introduces collaborative filtering methods including user‑based, item‑based and latent factor models, discusses similarity measures, power‑law distributions, and practical considerations such as negative sampling, providing a comprehensive overview for building recommendation systems.

Recommendation Systemscollaborative filteringlatent factor model
0 likes · 9 min read
Introduction to User Behavior and Collaborative Filtering in Recommendation Systems
Baidu Intelligent Testing
Baidu Intelligent Testing
Oct 9, 2017 · Big Data

User Behavior Analysis: From Data Acquisition to Funnel Insights

The article explains how to move beyond macro app metrics by collecting offline and real‑time user data, storing it in HDFS, processing it with Spark, visualizing behavior paths as state‑machine trees, and performing branch‑funnel analysis to uncover conversion bottlenecks and improve product quality.

AnalyticsBig DataFunnel Analysis
0 likes · 5 min read
User Behavior Analysis: From Data Acquisition to Funnel Insights
Qunar Tech Salon
Qunar Tech Salon
Feb 22, 2017 · Big Data

Understanding Ctrip Flight Ticket Tracking System (UBT) and Its Key Metrics

This article explains Ctrip's flight ticket tracking framework (UBT), detailing client‑side and server‑side event collection methods, the purpose and trade‑offs of each tracking type, metric definitions, data association challenges, common pitfalls, and best practices for reliable data‑driven analysis.

AnalyticsBig DataCtrip
0 likes · 20 min read
Understanding Ctrip Flight Ticket Tracking System (UBT) and Its Key Metrics
Ctrip Technology
Ctrip Technology
Sep 19, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature

This article describes Qunar's personalized demand prediction system for the "Guess You Like" card, detailing how user‑demand associations are mined via rule engines, collaborative filtering, LBS and manual rules, and how ranking models evolve from subjective Bayes to RankBoost and LambdaMart, with experimental evaluation and future LSTM plans.

AITravelmachine learning
0 likes · 10 min read
Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature
21CTO
21CTO
Mar 18, 2016 · Artificial Intelligence

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

This article outlines ten practical key points—including leveraging explicit and implicit feedback, hybridizing algorithms, handling temporal and geographic factors, exploiting social ties, solving cold‑start issues, optimizing presentation, defining clear metrics, ensuring real‑time updates, and scaling big‑data processing—to help engineers design effective intelligent recommendation systems.

cold startdata miningevaluation
0 likes · 18 min read
10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems
Qunar Tech Salon
Qunar Tech Salon
Apr 15, 2015 · Mobile Development

The Future of Mobile Messaging Platforms: Insights from Benedict Evans

Benedict Evans analyses how smartphones turned into social platforms, comparing WeChat's app‑as‑platform strategy with Facebook Messenger's message‑as‑app approach, and explores the structural challenges, user‑behavior dynamics, and potential future evolutions of mobile messaging ecosystems.

Facebook MessengerWeChatapp ecosystems
0 likes · 13 min read
The Future of Mobile Messaging Platforms: Insights from Benedict Evans