learning to rank

Qunar Tech Salon

Feb 17, 2025 · Artificial Intelligence

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

The article details Qunar’s hotel search ranking system evolution, covering the shift from rule‑based sorting to LambdaMart, the adoption of LambdaDNN deep models, multi‑objective MMOE architectures, multi‑scenario integration, extensive feature engineering, and experimental results demonstrating significant offline and online performance gains.

Recommendation systemsdeep learningfeature selection

0 likes · 36 min read

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

Architect

May 19, 2022 · Artificial Intelligence

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

This article details Amap's practical experience with Learning to Rank for search suggestions, covering application scenarios, data pipeline construction, feature engineering, model training, loss‑function adjustments, and the resulting performance improvements, while also discussing challenges such as sparse features and click bias.

AMapFeature EngineeringSearch Suggestion

0 likes · 9 min read

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

DataFunSummit

Mar 24, 2022 · Artificial Intelligence

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

This article provides a comprehensive introduction to Learning to Rank (LTR), describing its four major categories—point‑wise, pair‑wise, list‑wise, and generative models—along with typical algorithms such as Wide & Deep, ESMM, RankNet, LambdaRank, LambdaMART, DLCM, and miRNN, and discusses their architectures, loss functions, and practical considerations in advertising and recommendation systems.

Generative ModelsPairwisePointwise

0 likes · 22 min read

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

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.

DNNGBDTdeep learning

0 likes · 17 min read

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

Yuewen Technology

Nov 10, 2020 · Artificial Intelligence

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.

LambdaMARTLightGBMRecommendation systems

0 likes · 9 min read

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

DataFunTalk

Nov 9, 2020 · Artificial Intelligence

Practical Application of TensorFlow Ranking (TFR) in iQIYI Overseas Recommendation System

This article describes how iQIYI's overseas recommendation team adopted TensorFlow Ranking to replace traditional CTR models with Learning‑to‑Rank, detailing the framework’s architecture, challenges such as regularization and sequence feature support, the solutions implemented, and experimental results showing significant performance gains.

Artificial IntelligenceRecommendation systemsTensorFlow Ranking

0 likes · 15 min read

Practical Application of TensorFlow Ranking (TFR) in iQIYI Overseas Recommendation System

DataFunTalk

Jun 27, 2020 · Artificial Intelligence

Applying Learning to Rank for Search Suggestions at Gaode Maps

This article details how Gaode Maps leveraged machine‑learning‑based Learning to Rank to rebuild its search‑suggestion ranking pipeline, addressing challenges in sample construction, feature sparsity, and model optimization, and achieving significant improvements in relevance metrics and user experience.

Big DataFeature EngineeringGaode Maps

0 likes · 9 min read

Applying Learning to Rank for Search Suggestions at Gaode Maps

Ctrip Technology

Dec 12, 2019 · Artificial Intelligence

Applying XGBoost for Learning-to-Rank in Ctrip Search: Feature Engineering and Model Practice

This article details how Ctrip's search team leverages XGBoost for learning-to-rank, covering L2R concepts, feature engineering, data preparation, model training, hyper‑parameter tuning, offline evaluation, and deployment insights for large‑scale search ranking systems.

Feature EngineeringXGBoostlearning to rank

0 likes · 12 min read

Applying XGBoost for Learning-to-Rank in Ctrip Search: Feature Engineering and Model Practice

DataFunTalk

Aug 8, 2019 · Artificial Intelligence

Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform

This article shares JD's e‑commerce recommendation system practice, covering the overall online/offline architecture, recall and ranking modules, real‑time feature and model updates, multi‑objective and diversity strategies, first‑stage index‑based ranking, KNN recall, and a layered experiment platform for rapid iteration.

Rankinge-commercelearning to rank

0 likes · 14 min read

Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform

Ctrip Technology

Jun 19, 2019 · Artificial Intelligence

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

This article examines the limitations of traditional learning‑to‑rank for Ctrip hotel sorting, introduces reinforcement learning as a remedy, outlines three progressive implementation plans (A, B, C) with algorithm choices and engineering trade‑offs, and presents early experimental findings that demonstrate RL's potential to improve conversion rates.

CtripRLRanking

0 likes · 15 min read

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

Amap Tech

Jun 5, 2019 · Artificial Intelligence

Applying Learning to Rank for Search Suggestion Optimization at Gaode Maps

Gaode Maps applied Learning to Rank to optimize search suggestions, moving from rule-based to gradient boosted rank model, addressing sample construction and feature sparsity via session-based labeling and loss adjustment, achieving a seven‑point MRR gain and higher coverage, and paving the way for personalization and deep learning.

Gaode MapsSearch Suggestionlearning to rank

0 likes · 11 min read

Applying Learning to Rank for Search Suggestion Optimization at Gaode Maps

HomeTech

Oct 29, 2018 · Artificial Intelligence

Applying ListNet Listwise Ranking Model for Car Purchase Intent Prediction

This article introduces the ListNet listwise ranking algorithm, explains its theoretical foundations and loss function, presents a Python implementation with gradient computation, and demonstrates its superior performance over pointwise and pairwise models on public benchmarks and an internal automotive dataset for predicting users' intended car series.

Pythonlearning to ranklistnet

0 likes · 14 min read

Applying ListNet Listwise Ranking Model for Car Purchase Intent Prediction

vivo Internet Technology

Jan 22, 2018 · Artificial Intelligence

Learning to Rank: From Regression to Search Ranking and Evaluation Methods

Learning to rank reframes search as a machine‑learning problem that optimizes document ordering rather than numeric prediction, using relevance metrics such as NDCG and feature‑based scoring functions, and comparing point‑wise, pair‑wise (RankSVM) and list‑wise (ListNet) approaches while stressing that proper error definition and feature selection matter more than the specific algorithm.

NDCGPairwisePointwise

0 likes · 16 min read

Learning to Rank: From Regression to Search Ranking and Evaluation Methods

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.

Feature Engineeringhotel rankinglearning to rank

0 likes · 7 min read