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15 articles

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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.

Learning-to-RankRecommendation Systemsdeep-learning

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.

AIBaiduRecommendation Systems

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.

BaiduRecommendation Systemsfeature engineering

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.

AIEvaluation MetricsRecommendation Systems

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.

Evaluation Metricsinformation retrievalmodel distillation

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.

Deep LearningEvaluation MetricsSystem Architecture

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.

AdvertisingDiversityRerank

0 likes · 57 min read

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

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.

BM25information retrievalinverted index

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.

Learning-to-RankTensorFlow Rankingranking algorithms

0 likes · 15 min read

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

Alibaba Cloud Developer

Dec 17, 2019 · Artificial Intelligence

How Alibaba Boosted Short‑Video Engagement with Advanced Recommendation Algorithms

This article explains the rapid growth of short‑video on Taobao, describes the video feature framework, details the RankI2V and RankV2V recall methods, outlines coarse and fine ranking models, and presents real‑time interest and business strategies that significantly improved click‑through rates and viewing time.

Alibabamachine learningranking algorithms

0 likes · 19 min read

How Alibaba Boosted Short‑Video Engagement with Advanced Recommendation Algorithms

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.

Evaluation Metricscollaborative filteringcontent-based

0 likes · 17 min read

How Live-Streaming Platforms Build Scalable Recommendation Systems

21CTO

Sep 28, 2018 · Artificial Intelligence

Inside E‑Commerce Recommendation Systems: From Data Collection to Real‑Time Personalization

This article explains how e‑commerce recommendation systems work, covering regular and personalized recommendation types, the challenges of user profiling and data handling, the three‑stage recommendation pipeline, and the overall system architecture that powers real‑time, AI‑driven product suggestions.

AIdata pipelinee‑commerce

0 likes · 17 min read

Inside E‑Commerce Recommendation Systems: From Data Collection to Real‑Time Personalization

21CTO

Apr 9, 2018 · Artificial Intelligence

How E‑Commerce Platforms Build Effective Product Recommendation Systems

This article explains the fundamentals and advanced techniques of e‑commerce product recommendation systems, covering conventional and personalized approaches, user profiling, data collection, storage, modeling, the three‑stage pipeline of preprocessing, recall and ranking, as well as system architecture, challenges, and key algorithms such as LR and GBDT.

data pipelinee‑commercemachine learning

0 likes · 17 min read

How E‑Commerce Platforms Build Effective Product 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.

Boosted Decision TreesLambdaMARTRankNet

0 likes · 11 min read