bias correction

DataFunSummit

Mar 10, 2024 · Artificial Intelligence

Evaluating Long-Term Strategy Effects with A/B Experiments: Causes, Industry Solutions, and Business Cases

This article examines why A/B experiments often capture only short‑term impacts, explains external and internal factors behind short‑ and long‑term effects, and presents seven industrial methods—including user‑learning models, personalized recommendation adjustments, surrogate metrics, and bias correction—supported by real‑world case studies.

A/B testingbias correctioncausal inference

0 likes · 14 min read

Evaluating Long-Term Strategy Effects with A/B Experiments: Causes, Industry Solutions, and Business Cases

DataFunSummit

Feb 14, 2024 · Artificial Intelligence

Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios

This article presents Ant Group's research on causal debiasing for recommendation and marketing, covering the background of bias, common bias types, causal graph analysis, two correction approaches—data‑fusion based MDI and back‑door adjustment based DMBR—along with experimental results on public and proprietary datasets and real‑world deployment insights.

Ant GroupRecommendation systemsbias correction

0 likes · 16 min read

Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios

DataFunSummit

Jan 28, 2024 · Artificial Intelligence

Causal Inference and Bias Correction Methods in Ant Financial Risk Control

This article presents how Ant Group applies causal inference techniques—including confounding bias analysis, double‑difference methods, DiDTree, and shrinkage‑based causal trees—to correct biases in risk‑control scenarios, detailing the theoretical background, algorithmic designs, experimental validation, and practical deployment.

ant financialbias correctioncausal inference

0 likes · 21 min read

Causal Inference and Bias Correction Methods in Ant Financial Risk Control

DataFunSummit

Nov 24, 2023 · Artificial Intelligence

Cold-Start Content Recommendation Practices at Kuaishou

This article describes Kuaishou's approach to cold-start content recommendation, outlining the problems addressed, challenges in modeling sparse new videos, and solutions including graph neural networks, I2U retrieval, TDM hierarchical retrieval, bias correction, and future research directions.

Cold StartKuaishouRetrieval

0 likes · 19 min read

Cold-Start Content Recommendation Practices at Kuaishou

DataFunTalk

Jan 7, 2023 · Artificial Intelligence

How to Better Leverage Data in Causal Inference

This presentation introduces two recent works from Ant Group that improve causal inference by explicitly using historical control data to reduce selection bias and by fusing heterogeneous multi‑source data, describing the GBCT and WMDL methods, their theoretical foundations, experimental results, and practical applications in finance.

Financebias correctioncausal inference

0 likes · 18 min read

How to Better Leverage Data in Causal Inference

DataFunTalk

Nov 1, 2022 · Artificial Intelligence

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

This article presents the practical evolution of NetEase Yanxuan's recommendation pipeline, covering background, multi‑objective and cross‑domain modeling, bias correction, loss function enhancements, long‑term value strategies, and multi‑scene modeling, with experimental results and a Q&A session.

AICross-DomainMMoE

0 likes · 20 min read

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

Alimama Tech

Aug 3, 2022 · Artificial Intelligence

CausalMTA: Unbiased Multi‑Touch Attribution via Causal Inference

CausalMTA introduces a causal‑inference‑based multi‑touch attribution framework that eliminates static user‑attribute bias through variational recurrent auto‑encoder re‑weighting and dynamic preference bias via a gradient‑reversal‑layer‑augmented RNN, producing an unbiased conversion predictor whose Shapley‑value credits outperform existing methods on synthetic, Criteo, and Alibaba datasets.

Shapley valueadvertisingbias correction

0 likes · 15 min read

CausalMTA: Unbiased Multi‑Touch Attribution via Causal Inference