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

NetEase Yanxuan Technology Product Team

Aug 15, 2022 · Artificial Intelligence

Evolution of the First-Focus Personalized Recommendation Model in E-commerce

The article details a step‑by‑step evolution of an e‑commerce platform’s top‑slot recommendation system, moving from a DCN‑mix single‑objective model through BST‑based dynamic features, position‑bias debiasing, multi‑task MMoE learning, and finally BST with target‑attention, each yielding measurable CTR, conversion, and user‑value gains.

CTR predictionmulti-task learningposition bias

0 likes · 22 min read

Evolution of the First-Focus Personalized Recommendation Model in E-commerce

Mafengwo Technology

Mar 24, 2022 · Artificial Intelligence

How MaFengWo Reduces Position Bias in Its Recommendation Ranking System

This article explains how MaFengWo's recommendation ranking system tackles position bias by incorporating position features, using inverse propensity weighting, and adjusting click metrics, resulting in measurable improvements in click‑through rate, content exposure, and overall recommendation accuracy.

CTR predictioninverse propensity weightingposition bias

0 likes · 10 min read

How MaFengWo Reduces Position Bias in Its Recommendation Ranking System

Meituan Technology Team

Jun 10, 2021 · Artificial Intelligence

Deep Position-wise Interaction Network for CTR Prediction

The Meituan team introduces DPIN, a three‑module deep network that jointly models ads and their positions to mitigate position bias in CTR prediction, achieving up to 2.98% AUC improvement, 2.25% higher CTR and 2.15% RPM gains while keeping latency modest, and is applicable to broader ranking tasks.

AdvertisingCTR predictionDPIN

0 likes · 24 min read

Deep Position-wise Interaction Network for CTR Prediction

DataFunTalk

Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNDeep LearningGBDT

0 likes · 17 min read

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

Qunar Tech Salon

Aug 21, 2016 · Artificial Intelligence

Hotel Search Ranking: Problem Definition, Model Construction, Feature Engineering, and Offline Evaluation

This article presents a comprehensive overview of hotel search ranking, covering problem definition, the distinction between ranking and probability estimation, handling position bias, detailed feature engineering, the AnyBoost linear boosting model, offline evaluation methods, and observed online performance improvements.

Learning-to-Rankfeature engineeringhotel ranking

0 likes · 7 min read