ranking algorithms

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

Sohu Tech Products

Jan 10, 2024 · Artificial Intelligence

Baidu's Practices and Insights on Recommendation Ranking

Baidu’s recommendation ranking system handles billions of daily impressions and millions of users by combining discrete and cross features, bias mitigation, and long‑short sequence modeling within a multi‑stage funnel and hierarchical architecture, while planning to integrate large language models for generative, interpretable, and decision‑oriented recommendations.

AIBaiduFeature Engineering

0 likes · 19 min read

Baidu's Practices and Insights on Recommendation Ranking

DataFunTalk

Jan 7, 2024 · Artificial Intelligence

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

This article presents Baidu's comprehensive approach to feed recommendation ranking, covering business and data background, feature engineering principles, core algorithmic strategies, system architecture design, and upcoming plans to integrate large language models for more intelligent and fair recommendations.

BaiduFeature EngineeringRecommendation systems

0 likes · 19 min read

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

DataFunTalk

May 8, 2023 · Artificial Intelligence

Comprehensive Overview of Modern Recommendation System Technologies

This article presents a detailed survey of recent advances in recommendation system technology, covering system architecture, user understanding layers, various recall methods, ranking techniques, auxiliary algorithms such as cold-start and bias modeling, and evaluation metrics, with references to industry practices and academic research.

AIRecommendation systemsevaluation metrics

0 likes · 13 min read

Comprehensive Overview of Modern Recommendation System Technologies

DataFunTalk

Jan 18, 2023 · Artificial Intelligence

Search Relevance System Architecture and Practices in QQ Browser

This article presents the QQ Browser search relevance team's experience integrating QQ Browser and Sogou search systems, detailing business overview, relevance system evolution, algorithm architecture, evaluation metrics, deep semantic matching, relevance calibration, and model distillation techniques to improve search relevance performance.

Model DistillationSearch Relevancedeep learning

0 likes · 31 min read

Search Relevance System Architecture and Practices in QQ Browser

Tencent Cloud Developer

Jan 9, 2023 · Artificial Intelligence

Search Relevance Architecture and Practices in QQ Browser

The QQ Browser search relevance team describes a unified, billion‑scale architecture that combines a main and vertical subsystem, a pyramid‑shaped ranking pipeline (recall, coarse, fine), a dedicated GPU‑accelerated relevance service, and hybrid semantic‑matching models (dual‑tower, BERT, matrix fusion) evaluated with offline and online metrics to deliver accurate, fresh, and authoritative results for diverse content and long‑tail queries.

Search Relevancedeep learningevaluation metrics

0 likes · 28 min read

Search Relevance Architecture and Practices in QQ Browser

DataFunSummit

Apr 28, 2022 · Artificial Intelligence

ReRank: The Backstage of Recommendation Systems and Its Evolution Toward Ecosystem Reshaping

This article explores the role of ReRank in recommendation and advertising pipelines, detailing its algorithmic position, the challenges of diversity versus relevance, evaluation metrics such as DCG/NDCG, the evolution from heuristic methods to deep learning models, and practical insights from industry cases like Airbnb and Alibaba.

ReRankRecommendation systemsadvertising

0 likes · 57 min read

ReRank: The Backstage of Recommendation Systems and Its Evolution Toward Ecosystem Reshaping

DataFunSummit

Feb 3, 2022 · Artificial Intelligence

Insights into Recommendation Systems and Their Relation to Computational Advertising

This article examines how recommendation systems have evolved, highlighting the shared matching and ranking mechanisms with computational advertising while also identifying the distinct elements such as ad bidding and multi‑party objectives that differentiate advertising from pure recommendation.

Artificial IntelligenceData ModelingRecommendation systems

0 likes · 11 min read

Insights into Recommendation Systems and Their Relation to Computational Advertising

iQIYI Technical Product Team

Jul 30, 2021 · Artificial Intelligence

iQIYI Search Ranking Algorithm Practice – NLP and Search Integration

At iQIYI’s iTech Conference, Zhang Zhigang detailed a full‑stack search ranking system that combines NLP‑driven query analysis, hierarchical indexing, multi‑stage coarse‑to‑fine ranking, Transformer‑based re‑ranking, sparse‑feature DNN enhancements and LIME/SE‑Block explainability, delivering measurable gains in CTR and NDCG for the platform’s video search.

NLPiQIYIinformation retrieval

0 likes · 20 min read

iQIYI Search Ranking Algorithm Practice – NLP and Search Integration

DeWu Technology

Dec 4, 2020 · Fundamentals

Introduction to Search Engine Technology and Information Retrieval

The article surveys core search‑engine technology—document hierarchy, flat and vertical inverted indexes, query operators for building and merging score lists, and ranking models from Boolean and BM25 to language‑model approaches like Indri—providing a foundational overview of information retrieval.

BM25Inverted Indexinformation retrieval

0 likes · 14 min read

Introduction to Search Engine Technology and Information Retrieval

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

Efficient Ops

Mar 26, 2019 · Artificial Intelligence

How Live-Streaming Platforms Build Scalable Recommendation Systems

This article explains the design of a live‑streaming recommendation system, covering its overall architecture, ranking, content‑based and collaborative‑filtering methods, similarity calculations, multi‑algorithm fusion, sorting, user profiling, and evaluation metrics with practical examples and diagrams.

collaborative filteringcontent-basedevaluation metrics

0 likes · 17 min read

How Live-Streaming Platforms Build Scalable Recommendation Systems

Architects Research Society

Oct 16, 2015 · Artificial Intelligence

From RankNet to Boosted Decision Trees: Evolution of Bing’s Search Ranking Algorithms

Chris Burges recounts Microsoft’s transition from the early “Flying Dutchman” system to RankNet and finally to Boosted Decision Trees, explaining how fast experimentation, LambdaRank/MART innovations, and large‑scale data handling have dramatically improved Bing’s search ranking accuracy and efficiency.

Artificial IntelligenceBoosted Decision TreesLambdaMART

0 likes · 11 min read