BestHub
Sign in
Tagged articles
44 articles
Page 1 of 1
Ele.me Technology
Ele.me Technology
Nov 7, 2025 · Artificial Intelligence

LLM‑SM Hybrid Strategies: Boosting Decision Optimization and Store Design

Recent advances in large language models (LLMs) have sparked interest in their decision‑making capabilities, yet challenges remain; this article explores classic prediction‑optimization pipelines, introduces emerging LLM‑as‑Predictor/Ranker/Optimizer paradigms, and details practical case studies on delivery‑price optimization and intelligent store‑decoration recommendation using LLM‑SM hybrid systems.

Decision OptimizationHybrid ModelingLLM
0 likes · 30 min read
LLM‑SM Hybrid Strategies: Boosting Decision Optimization and Store Design
Alimama Tech
Alimama Tech
Aug 6, 2025 · Artificial Intelligence

How ComRecycle Cuts CPU/GPU Use by 23% in Taobao Ads: An Intelligent Computation Recycling Framework

This paper introduces ComRecycle, an intelligent computation recycling framework for Taobao's display advertising system that caches and reuses ad candidates across recall, coarse‑ranking, and fine‑ranking stages, achieving up to 23% CPU and 22% GPU savings while maintaining recommendation quality.

Resource OptimizationUplift Modelingcomputation recycling
0 likes · 17 min read
How ComRecycle Cuts CPU/GPU Use by 23% in Taobao Ads: An Intelligent Computation Recycling Framework
Didi Tech
Didi Tech
Jul 17, 2025 · Artificial Intelligence

How RAS‑AUCC Eliminates Offline‑Online Gaps in Multi‑Treatment Uplift Modeling

This article explains the challenges of evaluating uplift models for intelligent marketing with multiple discount treatments, reviews existing metrics such as AUUC, Qini, and AUCC, and introduces the RAS‑AUCC metric that aligns offline evaluation with online ROI by sorting samples by marginal ROI and using RCT data.

Evaluation MetricsMarketing OptimizationUplift Modeling
0 likes · 13 min read
How RAS‑AUCC Eliminates Offline‑Online Gaps in Multi‑Treatment Uplift Modeling
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 14, 2025 · Artificial Intelligence

Why Causal Inference Matters: From Theory to Real-World Uplift Models

This article explains the fundamentals of causal inference, distinguishes it from correlation, introduces major theoretical frameworks such as structural causal models and potential outcomes, and demonstrates practical uplift modeling techniques—including meta‑learners, double machine learning, and deep causal networks—through a financial credit‑limit use case.

Uplift Modelingcausal inferencedouble machine learning
0 likes · 17 min read
Why Causal Inference Matters: From Theory to Real-World Uplift Models
DataFunSummit
DataFunSummit
Jul 5, 2025 · Artificial Intelligence

Automating Causal Subpopulation Mining: Tencent Music’s Experiment Platform Breaks Down the Process

This article explains how Tencent Music’s experiment platform automates strategy‑positive subpopulation mining using unified dimension tables, CATE model training, double‑difference estimation, and propensity‑score matching, enabling rapid recommendation‑strategy optimization and data‑driven product decisions.

CATEExperiment PlatformUplift Modeling
0 likes · 17 min read
Automating Causal Subpopulation Mining: Tencent Music’s Experiment Platform Breaks Down the Process
DataFunSummit
DataFunSummit
Jun 21, 2025 · Artificial Intelligence

From Bias to Fairness: De‑biasing Techniques in Uplift Modeling

This article explores the fundamentals and challenges of uplift modeling, explains why unbiased random data are essential, and presents a comprehensive suite of bias‑correction methods—including reweighting, propensity‑score matching, and advanced deep‑learning architectures such as TarNet, CFRNet, and DragonNet—to improve causal effect estimation in marketing and finance applications.

Bias CorrectionDeep LearningUplift Modeling
0 likes · 15 min read
From Bias to Fairness: De‑biasing Techniques in Uplift Modeling
DataFunSummit
DataFunSummit
Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingUplift Modelingcausal inference
0 likes · 14 min read
Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice
DataFunSummit
DataFunSummit
May 11, 2024 · Artificial Intelligence

Why Causal Inference Matters in Machine Learning and Its Banking Applications

The article explains the necessity of incorporating causal relationships into machine learning, outlines the development of causal science, and details how uplift modeling and causal‑regularized stable learning are applied to marketing and risk control in the banking sector, while also discussing practical challenges and experimental results.

BankingUplift Modelingcausal inference
0 likes · 14 min read
Why Causal Inference Matters in Machine Learning and Its Banking Applications
DataFunTalk
DataFunTalk
Mar 12, 2024 · Artificial Intelligence

Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, including uplift modeling and entropy‑balancing with flexible spatiotemporal grids, to enhance marketing strategy efficiency using observational data while addressing industry‑specific technical challenges.

LogisticsMarketing OptimizationUplift Modeling
0 likes · 10 min read
Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry
DataFunSummit
DataFunSummit
Dec 24, 2023 · Artificial Intelligence

Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, especially uplift modeling combined with entropy‑balancing and flexible spatio‑temporal grid partitioning, to enhance marketing strategy efficiency, address confounding bias, and achieve stable, accurate effect estimation across diverse operational scenarios.

AILogisticsMarketing
0 likes · 10 min read
Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry
DataFunSummit
DataFunSummit
Dec 6, 2023 · Artificial Intelligence

Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices

Huya’s data‑driven experiment platform showcases how causal inference, AB testing, and uplift modeling are applied to advertising, user activation, and growth scenarios, detailing platform evolution, metric design, statistical challenges, and practical solutions such as multi‑test correction, CUPED, RTA, and propensity‑score methods.

AB testingData ScienceExperiment Platform
0 likes · 18 min read
Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices
DataFunTalk
DataFunTalk
Oct 31, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.

AIGCRecommendation SystemsUplift Modeling
0 likes · 22 min read
Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud
Ctrip Technology
Ctrip Technology
Jul 6, 2023 · Artificial Intelligence

Optimizing SMS Recall Marketing with Response and Uplift Models: A Ctrip Train Ticket Case Study

This article presents a comprehensive case study of Ctrip's train ticket SMS recall business, detailing the design, implementation, and evaluation of response‑based conversion rate models and uplift models to improve marketing ROI through causal inference and machine‑learning techniques.

Response ModelSMS MarketingUplift Modeling
0 likes · 14 min read
Optimizing SMS Recall Marketing with Response and Uplift Models: A Ctrip Train Ticket Case Study
DataFunSummit
DataFunSummit
Jun 27, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Their Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO's Andes Smart Cloud team's intelligent growth algorithm architecture, covering industry background, data pipelines, model designs such as uplift, PU‑learning, multimodal AIGC, and their practical applications in content supply, recommendation, precise audience targeting, and ad bidding, followed by a summary and Q&A.

AIGCMobile MarketingRTB
0 likes · 22 min read
Intelligent Growth Algorithms and Their Applications in the Smartphone Industry – OPPO Andes Smart Cloud
Didi Tech
Didi Tech
Jun 12, 2023 · Artificial Intelligence

Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets

Laser (Latent Surrogate Representation learning) estimates long‑term ride‑hailing market effects by inferring hidden surrogate variables from short‑term outcomes using an iVAE and inverse‑probability weighting, thereby reducing experiment cost and latency while achieving more accurate causal effect predictions than existing baselines.

IPWRide HailingUplift Modeling
0 likes · 9 min read
Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets
DataFunSummit
DataFunSummit
May 11, 2023 · Artificial Intelligence

Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices

This article introduces the application of causal inference in financial user operations, outlining typical scenarios such as programmatic advertising and user outreach, discussing data and business challenges, and presenting practical implementations including propensity score matching, sample library construction, experiment design, and full‑stack uplift modeling.

Data ChallengesFinancial MarketingUplift Modeling
0 likes · 14 min read
Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices
DataFunTalk
DataFunTalk
Apr 28, 2023 · Artificial Intelligence

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

This article explains how uplift sensitivity prediction, Bayesian causal networks, and decision‑path construction are applied to improve insurance product, coupon, and copy recommendations on the Fliggy platform, detailing modeling approaches, evaluation metrics, and practical outcomes of the causal inference framework.

AB testingBayesian networksInsurance Recommendation
0 likes · 16 min read
Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability
DataFunSummit
DataFunSummit
Apr 6, 2023 · Game Development

Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls

This article presents a data‑science‑focused guide on using causal inference and uplift models to drive overseas ad targeting and user‑operation decisions in games, covering audience selection, privacy‑aware exposure correction, bid optimization, experiment design pitfalls, network effects, and practical recommendations.

A/B testingAdvertisingUplift Modeling
0 likes · 18 min read
Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls
DataFunTalk
DataFunTalk
Mar 24, 2023 · Artificial Intelligence

Deep UPLIFT Modeling: Techniques, Challenges, and FinTech Applications

This article provides a comprehensive overview of deep UPLIFT models, covering their fundamentals, key technical challenges such as confounding bias and inductive bias, the evolution of meta‑learner and deep architectures, and practical case studies in financial technology marketing.

Deep LearningFinTechMarketing Optimization
0 likes · 14 min read
Deep UPLIFT Modeling: Techniques, Challenges, and FinTech Applications
DataFunSummit
DataFunSummit
Feb 5, 2023 · Artificial Intelligence

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

After attending the DataFun causal inference summit, this article outlines why causal analysis matters, its typical use cases, practical challenges, its relationship with A/B testing, and how it integrates with machine learning and deep learning to improve decision‑making and model robustness.

A/B testingDeep LearningUplift Modeling
0 likes · 10 min read
Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Jan 12, 2023 · Artificial Intelligence

How to Boost Beauty Brand Repeat Purchases with AI‑Driven Uplift Modeling

This article explains how beauty brands can increase repeat purchase rates by building high‑potential member prediction models, applying tiered segmentation, and leveraging various AI‑powered models—including natural repurchase, purchase power, marketing response, and uplift models—to optimize targeting, ROI, and overall sales performance.

AIUplift Modelingcustomer retention
0 likes · 20 min read
How to Boost Beauty Brand Repeat Purchases with AI‑Driven Uplift Modeling
DataFunTalk
DataFunTalk
Dec 22, 2022 · Artificial Intelligence

Causal Inference: Core Concepts, Differences from Traditional Machine Learning, and Real‑World Applications in Finance

This article introduces the fundamental ideas of causal inference, explains how it differs from correlation‑based machine learning, discusses the role of confounders, and showcases practical implementations in financial services such as offer optimization, uplift modeling, and decision‑making pipelines.

Financial AIUplift Modelingcausal inference
0 likes · 17 min read
Causal Inference: Core Concepts, Differences from Traditional Machine Learning, and Real‑World Applications in Finance
DataFunTalk
DataFunTalk
Aug 27, 2022 · Artificial Intelligence

User Growth Algorithms and Engineering Practices at Huya Live Streaming

This article details Huya's comprehensive user growth framework, covering the full acquisition‑activation‑retention‑revenue funnel, advertising workflow, crowd targeting stages, uplift modeling, virtual callbacks, intelligent bidding, and engineering implementations such as material automation, low‑latency RTA filtering, and dynamic strategy operators.

HuyaUplift Modelingadvertising algorithms
0 likes · 14 min read
User Growth Algorithms and Engineering Practices at Huya Live Streaming
HelloTech
HelloTech
May 26, 2022 · Artificial Intelligence

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Hello’s automated C‑side growth algorithm loop integrates diverse traffic sources, semi‑supervised PU‑learning, graph‑based look‑alike targeting, causal uplift models for smart subsidies, and adaptive copy and external ad optimization, dramatically boosting ride‑hailing and lifestyle service revenue while minimizing engineering duplication.

AI PlatformRecommendation SystemsUplift Modeling
0 likes · 20 min read
Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies
DataFunSummit
DataFunSummit
Apr 10, 2022 · Artificial Intelligence

Algorithmic Optimization of Information‑Flow Advertising for Hallo Mobility

This presentation details how Hallo Mobility tackles the challenges of information‑flow ad modeling by describing the ad ecosystem, the company’s business evolution, and the advertiser‑side algorithmic solutions—including plan‑level quality detection, creative‑level uplift modeling, feature‑cross engineering, and pre‑bid user screening—while outlining future directions for automated, data‑driven ad delivery.

AIInformation FlowUplift Modeling
0 likes · 18 min read
Algorithmic Optimization of Information‑Flow Advertising for Hallo Mobility
DataFunSummit
DataFunSummit
Apr 3, 2022 · Artificial Intelligence

Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility

This article explains how Hello Mobility uses tree‑based causal inference and uplift modeling to improve smart subsidy efficiency in hotel marketing, covering background, uplift methods, custom split criteria, offline AUUC evaluation, online deployment, and future research directions.

Marketing OptimizationUplift Modelingcausal inference
0 likes · 17 min read
Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility
HelloTech
HelloTech
Mar 28, 2022 · Artificial Intelligence

Algorithmic Optimization for Information Flow Advertising at Hello Travel

Hello Travel tackles information‑flow advertising challenges by using LightGBM‑based models to predict order conversion, creative performance, and pre‑bid user quality, augmenting sparse data with feature engineering and uplift techniques, while planning future fully automated delivery, richer pre‑screening, and cross‑channel reinforcement‑learning enhancements.

AdvertisingAlgorithm OptimizationLightGBM
0 likes · 18 min read
Algorithmic Optimization for Information Flow Advertising at Hello Travel
DataFunTalk
DataFunTalk
Mar 27, 2022 · Artificial Intelligence

Algorithmic Optimization for Information‑Flow Advertising at Hello Travel

This talk explains how Hello Travel tackles challenges in information‑flow advertising by describing the market landscape, their business background, and detailed algorithmic optimization across plan, creative, and pre‑bid dimensions, including data‑driven modeling, feature engineering, LightGBM and uplift models, and outlines future directions.

AdvertisingAlgorithm OptimizationLightGBM
0 likes · 16 min read
Algorithmic Optimization for Information‑Flow Advertising at Hello Travel
HelloTech
HelloTech
Mar 1, 2022 · Artificial Intelligence

Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services

The paper applies causal inference and tree‑based uplift modeling to identify coupon‑responsive riders, using T‑, S‑, and X‑Learners as well as a proprietary Treelift model that directly optimizes per‑user utility, achieving a 4.7% lift over manual rules and 2.3% over prior response models.

AIMarketing OptimizationUplift Modeling
0 likes · 17 min read
Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services
DataFunTalk
DataFunTalk
Feb 28, 2022 · Artificial Intelligence

Causal Inference and Uplift Modeling for Intelligent Subsidy in Hotel Marketing at Hello Mobility

This article explains how Hello Mobility applies causal inference and tree‑based uplift models to improve the efficiency of hotel‑marketing subsidies, detailing background, problem formulation, various uplift learning methods, a customized TreeCausal (Treelift) algorithm, offline AUUC evaluation, online deployment, and future research directions.

AIMarketing OptimizationUplift Modeling
0 likes · 17 min read
Causal Inference and Uplift Modeling for Intelligent Subsidy in Hotel Marketing at Hello Mobility
DataFunTalk
DataFunTalk
Feb 7, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can detect subtle correlations in large datasets, improve user growth metrics such as retention and activity, and presents practical methods like propensity score matching, uplift modeling, HTE analysis, and meta‑learners applied to recommendation systems.

Propensity Score MatchingUplift Modelingheterogeneous treatment effect
0 likes · 13 min read
Causal Machine Learning for User Growth: Concepts, Methods, and Applications
DataFunTalk
DataFunTalk
Dec 30, 2021 · Artificial Intelligence

Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser

This article details Tencent's application of uplift modeling to optimize QQ Browser push notification volume, covering push system characteristics, causal analysis challenges, a refined S‑learner with metric learning, and resulting DAU improvements, while also addressing practical Q&A on uplift techniques.

DAU optimizationPush NotificationTencent
0 likes · 8 min read
Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser
DataFunSummit
DataFunSummit
Dec 26, 2021 · Artificial Intelligence

Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies

This article introduces the fundamentals of causal inference with observational data, explains confounding and collider structures, compares observational and experimental approaches, discusses challenges such as Simpson’s paradox, and presents Tencent’s quasi‑experimental applications including DID, regression discontinuity, and uplift modeling.

DIDPropensity Score MatchingQuasi-experiment
0 likes · 26 min read
Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies
DataFunTalk
DataFunTalk
Dec 6, 2021 · Artificial Intelligence

Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies

This article provides a comprehensive overview of causal inference on observational data, explaining confounding and collider structures, experimental solutions, the differences between observational and experimental data, challenges such as Simpson's paradox, and detailed Tencent case studies using DID, regression discontinuity, and uplift modeling to guide practical analysis.

DIDQuasi-experimentUplift Modeling
0 likes · 26 min read
Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies
DataFunSummit
DataFunSummit
Sep 12, 2021 · Artificial Intelligence

Algorithmic Practices in Haola Ride-Sharing: Platform Infrastructure, Matching Recommendation Engine, Transaction Governance, and Intelligent Marketing

This article details Haola's end‑to‑end algorithmic ecosystem for its ride‑sharing service, covering the machine‑learning platform built on Hadoop/YARN, the architecture and evolution of the matching recommendation engine, transaction‑ecosystem governance models, and intelligent marketing strategies including uplift modeling and optimization.

AIRecommendation EngineRide-sharing
0 likes · 19 min read
Algorithmic Practices in Haola Ride-Sharing: Platform Infrastructure, Matching Recommendation Engine, Transaction Governance, and Intelligent Marketing
HelloTech
HelloTech
Sep 10, 2021 · Artificial Intelligence

Algorithmic Practices in Haolo Carpool Service: Platform, Matching Engine, Transaction Governance, and Intelligent Marketing

The article details Haolo's end-to-end AI platform—built on Hadoop/Yarn with Spark ML, XGBoost and TensorFlow—and explains how its matching recommendation engine, transaction-ecosystem governance models, and intelligent uplift-based marketing system jointly boost carpool efficiency, safety, user retention, and ROI.

AI AlgorithmsRide-sharingTransaction Governance
0 likes · 19 min read
Algorithmic Practices in Haolo Carpool Service: Platform, Matching Engine, Transaction Governance, and Intelligent Marketing
DataFunTalk
DataFunTalk
May 19, 2021 · Artificial Intelligence

Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads

This article explains how causal inference techniques are applied to model the uplift effect of ad placement in Alibaba's Fliggy search CPC advertising, transforming budget allocation into a multi‑objective optimization problem and describing practical control methods, feature engineering, sample re‑sampling, model designs, uplift evaluation, and future research directions.

AdvertisingUplift Modelingbudget allocation
0 likes · 18 min read
Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads
DataFunTalk
DataFunTalk
May 13, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

This article introduces the Continuous Causal Forest, a novel uplift modeling approach that expands binary treatment effect estimation to handle multivariate and continuous treatment variables, demonstrates its construction, evaluates its performance on ride‑hailing pricing strategies, and discusses its advantages, limitations, and future research directions.

Pricing strategyUplift Modelingcausal forest
0 likes · 9 min read
Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments
Beike Product & Technology
Beike Product & Technology
Sep 26, 2020 · Artificial Intelligence

Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications

This article introduces uplift (incremental) modeling as a causal inference technique for intelligent marketing, explains its mathematical formulation, compares response and uplift models, describes various modeling approaches such as two‑model, one‑model, and label‑transformation methods, outlines evaluation metrics like Qini and AUUC, and demonstrates practical deployment in a real‑world real‑estate platform.

A/B testingQini curveUplift Modeling
0 likes · 21 min read
Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications
Youku Technology
Youku Technology
Apr 2, 2020 · Artificial Intelligence

In‑Depth Overview of Intelligent Marketing Uplift Modeling

The talk explains uplift modeling for intelligent marketing, showing how causal lift predictions—derived from randomized experiments using two‑model, one‑model, or tree‑based methods—identify truly responsive users, evaluate performance with AUUC/Qini, and were applied to Taopiaopiao’s coupon allocation via knapsack optimization, highlighting challenges and future directions.

A/B testingUplift Modelingcausal inference
0 likes · 16 min read
In‑Depth Overview of Intelligent Marketing Uplift Modeling
DataFunTalk
DataFunTalk
Mar 25, 2020 · Artificial Intelligence

Uplift Modeling for Intelligent Marketing: Principles, Methods, and Taopiaopiao Ticket Subsidy Case

The article explains how uplift models address the core challenge of measuring incremental marketing impact, outlines common modeling and evaluation techniques, and demonstrates a practical implementation in Taopiaopiao's smart ticket subsidy system, highlighting data collection, algorithm design, calibration, and future research directions.

Marketing OptimizationUplift Modeling
0 likes · 17 min read
Uplift Modeling for Intelligent Marketing: Principles, Methods, and Taopiaopiao Ticket Subsidy Case