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AntTech
AntTech
May 15, 2025 · Artificial Intelligence

Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference

This announcement introduces a live session that will dissect two best‑paper award research works from WSDM 2025—one revealing how recommendation models amplify popularity bias through spectral analysis and proposing a lightweight regularizer, and the other presenting a graph disentangle causal model that integrates GNNs with structural causal models to improve causal inference on networked observational data.

Recommendation SystemsWSDM 2025causal inference
0 likes · 4 min read
Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 11, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments

This article introduces causal inference concepts, explains structural causal and potential‑outcome frameworks, presents the MACR model for debiasing popularity in recommender systems, and details two experiments conducted on the ZhaiZhai platform along with future research directions.

MACRcausal inferencecounterfactual reasoning
0 likes · 13 min read
Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments
DataFunTalk
DataFunTalk
Jun 4, 2023 · Artificial Intelligence

Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems

This presentation introduces a decoupled domain‑adaptation network that separates popularity and attribute representations to mitigate popularity bias in recommender systems, describing the problem, existing IPS and causal‑inference solutions, the CD2AN architecture, experimental results, and practical Q&A.

AIdomain adaptationmachine learning
0 likes · 13 min read
Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems
Kuaishou Tech
Kuaishou Tech
Apr 25, 2023 · Artificial Intelligence

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

The paper introduces DCCL, a model‑agnostic contrastive learning framework that decouples user interest and conformity representations to address popularity bias and out‑of‑distribution challenges in recommendation systems, demonstrating significant offline and online performance gains on real‑world datasets.

OOD robustnesscausal inferencecontrastive learning
0 likes · 8 min read
DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems
DaTaobao Tech
DaTaobao Tech
Mar 24, 2023 · Artificial Intelligence

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

In a March 27 livestream, Alibaba senior algorithm engineer Chen Zhihong will explain how popularity bias affects recommendation pipelines, review existing mitigation techniques, and introduce a decoupled domain‑adaptive unbiased dual‑tower recall model that leverages bias while preserving recommendation fairness.

Recommendation SystemsUnbiased Recallmachine learning
0 likes · 2 min read
Leveraging Popularity Bias with Decoupled Unbiased Recall Models
JD Cloud Developers
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
DaTaobao Tech
DaTaobao Tech
May 31, 2022 · Artificial Intelligence

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

The paper proposes CDAN, a dual‑tower retrieval model that separates item attribute and popularity representations via a Feature Decoupling Module with orthogonal embeddings, aligns head‑tail attribute distributions using MMD and contrastive learning, and jointly trains biased and unbiased towers, achieving higher tail recall, lower exposure concentration, and measurable online click‑through improvements.

Recommendation Systemscontrastive learningdomain adaptation
0 likes · 13 min read
Decoupling Popularity Bias in Dual‑Tower Retrieval Models