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Nov 6, 2025 · Artificial Intelligence

Mastering Few-Shot Prompting: Principles, Bias Fixes, and Example Design

Few-shot prompting uses a handful of task examples within the prompt to guide large language models, improving performance, adaptability, and reducing data needs, while careful design of example quantity, order, label distribution, format, and bias mitigation—through calibration and advanced methods like reinforced and unsupervised ICL—optimizes results.

Prompt engineeringbias mitigationexample design

0 likes · 11 min read

Mastering Few-Shot Prompting: Principles, Bias Fixes, and Example Design

DataFunSummit

Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

bias mitigationcausal inferenceinterest disentanglement

0 likes · 24 min read

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

DataFunSummit

May 1, 2024 · Artificial Intelligence

Causal Solutions for Recommendation System Bias and Practical Applications

This article presents causal inference–based methods to address bias in recommendation systems, covering the transformation of recommendation problems into causal problems, selection bias mitigation through double‑robust and multi‑robust learning, individual treatment effect estimation, and a case study on attention bias in music recommendation.

bias mitigationcausal inferencedouble robust learning

0 likes · 12 min read

Causal Solutions for Recommendation System Bias and Practical Applications

Sohu Tech Products

Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

bias mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

DataFunTalk

Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

bias mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

DataFunTalk

Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIRecommendation Systemsbias mitigation

0 likes · 3 min read

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

21CTO

Mar 10, 2023 · Artificial Intelligence

Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights

The interview with OpenAI Robotics researcher Lilian reveals the team’s gender makeup, her work on robot hands, reinforcement‑learning breakthroughs, applied AI safety projects, bias mitigation efforts, and how personal learning blogs fuel continuous innovation in artificial intelligence.

AGIOpenAIRobotics

0 likes · 11 min read

Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights

JD Cloud Developers

Feb 27, 2023 · Artificial Intelligence

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

The article explains JD’s Explore & Exploit (EE) module, its bias‑related challenges, the iterative optimization loop, model debiasing techniques for position and popularity bias, personalized bias modeling, causal inference methods, online AB results, and offline evaluation metrics, highlighting significant improvements in search diversity and efficiency.

EE moduleRecommendation Systemsbias mitigation

0 likes · 16 min read

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

DataFunSummit

Feb 23, 2023 · Artificial Intelligence

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

This article provides a comprehensive overview of causal inference, explaining its definition, the distinction between correlation and causation, classic pitfalls such as Simpson's paradox, key metrics like ATE and ATT, experimental designs, bias mitigation techniques, and practical case studies from content platforms and the Titanic dataset.

A/B testingbias mitigationcausal inference

0 likes · 22 min read

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

This article presents a comprehensive overview of the background, current multi‑path recall methods, and a series of practical optimizations—including dual‑tower models, enhanced vectors, an unbiased IPW‑based framework, and a travel‑state‑aware deep recall model—applied to Feizhu's homepage recommendation system, with both offline and online experimental results demonstrating click‑through rate improvements.

Recommendation Systemsbias mitigationdual-tower model

0 likes · 17 min read

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

Alimama Tech

Dec 14, 2022 · Artificial Intelligence

Contrastive Image Representation Learning with Debiasing for CTR Prediction

The article proposes a three-stage contrastive learning framework—pre‑training, fine‑tuning, and debiasing—to generate unbiased, fine‑grained image embeddings for mobile Taobao CTR prediction, achieving higher accuracy, fairness, and a 4‑5% CTR lift in large‑scale offline and online evaluations.

CTR predictionDeep Learningbias mitigation

0 likes · 14 min read

Contrastive Image Representation Learning with Debiasing for CTR Prediction

Zhuanzhuan Tech

Oct 14, 2022 · Artificial Intelligence

Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization

The article explains how recommendation systems balance exploitation and exploration, details various bias sources such as selection, exposure, conformity, and position bias, presents mitigation techniques like feature input, bias towers, and greedy algorithms, and discusses diversity‑focused exploration using DPP methods.

DiversityExploration-Exploitationbias mitigation

0 likes · 7 min read

Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization

DataFunTalk

Jun 24, 2022 · Artificial Intelligence

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

The talk presents JD Search's Explore‑and‑Exploit (EE) module, detailing its bias‑mitigation pipeline—including position, popularity, and exposure debiasing—model architecture upgrades with SVGP and causal inference, online AB metrics, offline evaluation methods, and future research directions to improve search diversity and long‑term value.

SVGPbias mitigationexplore‑exploit

0 likes · 17 min read

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

DataFunSummit

May 26, 2022 · Artificial Intelligence

Exploring Contrastive Learning in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how contrastive learning can alleviate data sparsity and distribution bias in recommendation systems, detailing its theoretical advantages, recent research progress in computer vision and NLP, and a multi‑task self‑supervised framework applied to Kuaishou's short‑video ranking pipeline with significant offline and online performance gains.

AIKuaishouRecommendation Systems

0 likes · 21 min read

Exploring Contrastive Learning in Kuaishou Recommendation Systems

Kuaishou Tech

Feb 24, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how causal inference techniques are applied to identify and correct various biases in Kuaishou's recommendation pipeline, covering background theory, recent research, practical implementations such as popularity debias, causal embedding decoupling, and video completion‑rate debias, along with experimental results and future challenges.

EmbeddingKuaishoubias mitigation

0 likes · 19 min read

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

DataFunTalk

Feb 21, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This talk explains how recommendation bias arises from popularity and position effects, introduces causal inference concepts and three inference levels, reviews recent research such as DICE and Huawei’s causal embedding, and details Kuaishou’s practical applications—including popularity debias, causal representation decoupling, and video completion‑rate debias—along with experimental results and future challenges.

Kuaishoubias mitigationmachine learning

0 likes · 20 min read

DataFunTalk

Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Recommendation SystemsTransformerbias mitigation

0 likes · 22 min read

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

Meituan Technology Team

Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingGraph ModelingKDD Cup

0 likes · 23 min read

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

Tencent Cloud Developer

Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Embeddingbias mitigationdata preprocessing

0 likes · 32 min read

Feature Engineering Practices for Short‑Video Recommendation Systems

JD.com Experience Design Center

Jun 28, 2019 · R&D Management

How Cognitive Biases Skew User Research—and How to Counteract Them

This article explains common cognitive biases that affect user research—such as friendliness, social desirability, bandwagon, Hawthorne, anchoring, and peak‑end effects—and provides practical strategies like combining backend data, reducing participant concerns, minimizing external influences, and probing deeper to obtain more reliable, objective insights.

R&DUXUser Research

0 likes · 11 min read

How Cognitive Biases Skew User Research—and How to Counteract Them