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AI Engineer Programming
AI Engineer Programming
Apr 20, 2026 · Artificial Intelligence

Evaluating Retriever Quality in RAG: Essential Metrics for Production Reliability

The article explains why retrieval quality dominates RAG performance and outlines a rigorous evaluation framework—including prompt, ranked results, and ground‑truth annotations—and detailed metrics such as Precision, Recall, MAP@K, NDCG@K, MRR, and F‑scores, while discussing chunking strategies, embedding choices, hybrid retrieval, and CI/CD‑driven monitoring to ensure production reliability.

LLMMAPNDCG
0 likes · 12 min read
Evaluating Retriever Quality in RAG: Essential Metrics for Production Reliability
Alimama Tech
Alimama Tech
May 12, 2025 · Artificial Intelligence

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising

The article presents the Universal Recommendation Model (URM), a large‑language‑model‑based recall framework that integrates world knowledge and e‑commerce expertise through knowledge injection and prompt‑driven alignment, achieving significant offline recall gains and a 3.1% increase in ad consumption while meeting high‑QPS, low‑latency production constraints.

AdvertisingPrompt engineeringhigh QPS
0 likes · 17 min read
Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising
JD Retail Technology
JD Retail Technology
Sep 4, 2024 · Artificial Intelligence

Multimodal Recommendation Algorithms and System Architecture at JD.com

This article presents JD.com’s multimodal recommendation system architecture, covering content understanding, multimodal ranking and recall models, practical deployment pipelines, and future research directions such as large‑model integration and supply‑side generation, all illustrated with detailed diagrams and Q&A.

AIJD.comcontent understanding
0 likes · 14 min read
Multimodal Recommendation Algorithms and System Architecture at JD.com
Ximalaya Technology Team
Ximalaya Technology Team
Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural Networkmachine learning
0 likes · 18 min read
Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems
Alimama Tech
Alimama Tech
May 29, 2024 · Artificial Intelligence

Mixture of Multi‑Modal Experts for Advertising Recall

The Mixed‑Modal Expert Model combines ID features with image and text embeddings through optimized representations and conditional output fusion, dramatically improving advertising recall—especially for long‑tail items—and delivering measurable gains in click‑recall, revenue, CTR, and page views in large‑scale online tests.

Modelmachine learningmultimodal
0 likes · 15 min read
Mixture of Multi‑Modal Experts for Advertising Recall
DataFunSummit
DataFunSummit
Oct 4, 2023 · Artificial Intelligence

Comprehensive Overview of Recommendation System Technologies and Their Evolution

This article provides a detailed overview of modern recommendation system technology, covering system architecture, user understanding layers, various recall and ranking techniques, additional algorithmic directions such as cold‑start and bias modeling, and the evolving evaluation metrics used in practice.

Recommendation Systemsbias modelingrecall
0 likes · 14 min read
Comprehensive Overview of Recommendation System Technologies and Their Evolution
JD Cloud Developers
JD Cloud Developers
Aug 22, 2023 · Artificial Intelligence

A Practical Guide to Recommendation System Architecture and Methods

This article provides a concise overview of recommendation systems, covering their definition, core framework of recall, ranking, and re‑ranking, various recall strategies including multi‑path and vector‑based methods, similarity calculations, and practical implementation details such as AB testing and code examples.

AB testingVector Embeddinginformation retrieval
0 likes · 14 min read
A Practical Guide to Recommendation System Architecture and Methods
JD Retail Technology
JD Retail Technology
Aug 18, 2023 · Artificial Intelligence

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

This article provides a comprehensive overview of recommendation systems, covering their definition, basic framework, request flow, AB testing, recall strategies (both non‑personalized and personalized), collaborative‑filtering methods, vector‑based retrieval, wide‑and‑deep models, and the MMR re‑ranking algorithm with code examples.

Vector Retrievalcollaborative filteringmachine learning
0 likes · 14 min read
Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking
Bitu Technology
Bitu Technology
Aug 2, 2023 · Artificial Intelligence

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

This article details Tubi's multi‑stage recommendation system, focusing on the recall phase and describing how popularity metrics, embedding averaging, per‑video nearest‑neighbors, hierarchical clustering, real‑time ranking, and context‑aware sampling are combined to efficiently generate personalized video candidates at scale.

EmbeddingRecommendation SystemsVideo Streaming
0 likes · 10 min read
Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations
DeWu Technology
DeWu Technology
Jul 24, 2023 · Artificial Intelligence

Design and Implementation of a Word Distribution Platform for Personalized Recommendations

The paper presents a unified word‑distribution platform that delivers personalized bottom‑words, hot‑words, and drop‑down suggestions across e‑commerce domains, detailing its preprocessing, recall, fusion, ranking, and re‑ranking pipelines, C++ engine migration, script hot‑deployment, visual configuration tools, and stability mechanisms for scalable, low‑maintenance guide services.

AISystem ArchitectureWord Distribution
0 likes · 23 min read
Design and Implementation of a Word Distribution Platform for Personalized Recommendations
dbaplus Community
dbaplus Community
Jul 19, 2023 · Artificial Intelligence

How Xianyu Built a Scalable Recommendation Platform for 10+ Scenarios

This article explains how Xianyu’s product recommendation system tackles massive data, diverse business scenarios, and engineering challenges by designing a unified recommendation middle‑platform that abstracts data, recall, ranking, and re‑ranking stages, enabling rapid scene onboarding and scalable model iteration.

AIplatformranking
0 likes · 14 min read
How Xianyu Built a Scalable Recommendation Platform for 10+ Scenarios
Alimama Tech
Alimama Tech
Feb 8, 2023 · Artificial Intelligence

Evolution of Recall Indexes in Alibaba Advertising: From Quantization to Graph-based HNSW

Alibaba’s advertising pipeline progressed from low‑dimensional quantization partitions to hierarchical tree indexes, then to graph‑based HNSW structures—including multi‑category, multi‑level graphs and a BlazeOp‑driven scoring service—dramatically boosting recall efficiency, scalability and maintainability while meeting strict latency constraints.

HNSWlarge scalerecall
0 likes · 13 min read
Evolution of Recall Indexes in Alibaba Advertising: From Quantization to Graph-based HNSW
DataFunSummit
DataFunSummit
Jan 25, 2023 · Artificial Intelligence

Expert Insights on Recommendation System Architecture, Data, Features, Recall, Ranking and Evaluation

This interview compiles expert opinions on the end‑to‑end recommendation system pipeline—including architecture, data collection, user profiling, content structuring, feature engineering, recall strategies, ranking algorithms, multi‑objective optimization, multi‑modal fusion, re‑ranking, cold‑start solutions, evaluation metrics and real‑world applications—highlighting the technical challenges and practical solutions.

Evaluation Metricscold startfeature engineering
0 likes · 15 min read
Expert Insights on Recommendation System Architecture, Data, Features, Recall, Ranking and Evaluation
DataFunTalk
DataFunTalk
Jan 21, 2023 · Artificial Intelligence

Challenges and Best Practices in Recommendation Systems – Expert Interview

This interview with three recommendation‑system experts explores the technical architecture, data sources, feature engineering, recall and ranking strategies, evaluation metrics, cold‑start solutions, and practical difficulties, offering actionable insights to avoid common pitfalls in real‑world recommender deployments.

Evaluation MetricsRecommendation Systemscold start
0 likes · 15 min read
Challenges and Best Practices in Recommendation Systems – Expert Interview
政采云技术
政采云技术
Jan 4, 2023 · Artificial Intelligence

Overview of Recommendation and Search System Architecture: Recall and Ranking Techniques

This article explains the architecture of recommendation and search systems, detailing various recall methods such as collaborative filtering, matrix factorization, and vector‑based approaches, as well as ranking models like LR, FM, and DeepFM, and discusses re‑ranking and traffic control strategies.

artificial intelligencerankingrecall
0 likes · 14 min read
Overview of Recommendation and Search System Architecture: Recall and Ranking Techniques
Model Perspective
Model Perspective
Jun 22, 2022 · Artificial Intelligence

Understanding Model Performance: Precision, Recall, and F1 Score Explained

This article explains how to evaluate classification models by moving beyond simple accuracy to using confusion matrices, precision, recall, and the F1 score, illustrating their trade‑offs and when each metric is most appropriate for different real‑world scenarios.

F1 scoreclassificationconfusion matrix
0 likes · 4 min read
Understanding Model Performance: Precision, Recall, and F1 Score Explained
DeWu Technology
DeWu Technology
Apr 18, 2022 · Artificial Intelligence

Warehouse Storage Location Recommendation: Architecture, Recall, and Ranking Strategies

The article outlines DeWu’s warehouse‑management recommendation system, which combines an online‑near‑line‑offline architecture to quickly recall viable shelf slots and rank them by space utilization, travel time, and sales potential, enabling automated, constraint‑aware placement that cuts picking time and inventory costs.

AIBig DataStorage Optimization
0 likes · 16 min read
Warehouse Storage Location Recommendation: Architecture, Recall, and Ranking Strategies
Tencent Cloud Developer
Tencent Cloud Developer
Apr 11, 2022 · Artificial Intelligence

Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization

The recall module in recommendation systems retrieves thousands of items from massive pools using parallel non-personalized and personalized paths—such as hot-item, content-based, behavior-based, and deep-model recall—prioritizing coverage and low latency while addressing challenges like hard-negative sampling, selection bias, objective alignment, and channel competition to feed downstream ranking.

AImachine learningmulti-path retrieval
0 likes · 15 min read
Recall Module in Recommendation Systems: Multi-Path Retrieval and Optimization
58 Tech
58 Tech
Dec 16, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58's commercial recruitment recommendation system, covering the business scenario, system architecture, regional and behavior‑based recall methods, various ranking models—including coarse‑ranking, dual‑tower, DIN‑bias, and multitask W3DA—and future optimization directions.

DBSCANEGESonline advertising
0 likes · 20 min read
Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques
DataFunTalk
DataFunTalk
Dec 12, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58’s commercial recruitment recommendation system, covering the characteristics of the app’s recommendation scenario, system architecture, region‑based and behavior‑based recall methods, and coarse‑ and fine‑ranking models with various optimizations and future directions.

AIe‑commercemachine learning
0 likes · 20 min read
Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques
DataFunSummit
DataFunSummit
Dec 12, 2021 · Artificial Intelligence

Design and Implementation of 58.com Commercial Recruitment Recommendation System

This article presents a comprehensive overview of the 58.com commercial recruitment recommendation system, detailing its business challenges, system architecture, region‑based and behavior‑based recall strategies, coarse‑ and fine‑ranking models, bias handling, evaluation methods, and future directions.

CTRDBSCANEGES
0 likes · 20 min read
Design and Implementation of 58.com Commercial Recruitment Recommendation System
58 Tech
58 Tech
Nov 25, 2021 · Artificial Intelligence

Technical Evolution of the “Guess You Want” Recommendation Module in 58 Local Services

This article describes the design, multi‑stage recall strategies, and successive ranking model upgrades—including BERT‑based intent prediction, vector‑based DSSM recall, tag expansion, and multi‑task DeepFM/MMoE/ESMM architectures—that together reduce no‑result rates and significantly improve user conversion for 58's local service platform.

BERTDSSMmulti-task learning
0 likes · 16 min read
Technical Evolution of the “Guess You Want” Recommendation Module in 58 Local Services
DataFunTalk
DataFunTalk
May 17, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning Model Evaluation Metrics

This article provides a comprehensive summary of machine learning model evaluation metrics, covering accuracy, precision, recall, F1, RMSE, ROC/AUC, KS test, and scoring cards, with explanations, formulas, code examples, and practical considerations for model performance assessment.

AUCKSModel Evaluation
0 likes · 19 min read
Comprehensive Overview of Machine Learning Model Evaluation Metrics
DataFunTalk
DataFunTalk
May 15, 2021 · Artificial Intelligence

Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation

The article reviews the evolution of iQIYI's short‑video recommendation recall pipeline, detailing multi‑interest recall methods such as clustering‑based recall, MOE‑based recall, single‑activation multi‑interest networks, regularization strategies, dynamic capacity handling, and multimodal extensions, and discusses their impact on recommendation performance.

TransformeriQIYImachine learning
0 likes · 15 min read
Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation
DataFunTalk
DataFunTalk
Apr 29, 2021 · Artificial Intelligence

Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall

This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.

Deep LearningEmbeddingPDN
0 likes · 21 min read
Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall
Sohu Tech Products
Sohu Tech Products
Apr 14, 2021 · Artificial Intelligence

Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study

This tutorial walks through a complete workflow for assessing machine‑learning models—using a Kaggle HR attrition dataset, comparing Random Forest and Gradient Boosting via ROC‑AUC, precision, recall and segment analysis with the Evidently library—to decide which model is ready for production deployment.

Model EvaluationROC AUCemployee attrition
0 likes · 17 min read
Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study
DataFunTalk
DataFunTalk
Apr 2, 2021 · Artificial Intelligence

Engineering Practices of the K‑Song Recommendation System at Tencent Music

This article presents a comprehensive technical overview of the K‑Song recommendation platform, covering its backend architecture, the evolution of recall strategies, feature management and ranking pipelines, large‑scale deduplication techniques, and the debugging and monitoring infrastructure that support high‑performance personalized music recommendations.

DebuggingK‑SongTencent Music
0 likes · 23 min read
Engineering Practices of the K‑Song Recommendation System at Tencent Music
DataFunTalk
DataFunTalk
Feb 3, 2021 · Artificial Intelligence

Travel Search Technology and Innovations at Alibaba Feizhu

This article presents an in‑depth overview of Alibaba Feizhu's travel‑scene search system, covering its background, architecture, query understanding, tagging, POI mining, synonym extraction, recall strategies, model designs, performance results, and future directions for personalization and explainability.

AINLPSearch
0 likes · 18 min read
Travel Search Technology and Innovations at Alibaba Feizhu
DeWu Technology
DeWu Technology
Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Deep LearningEmbeddingRecommendation Systems
0 likes · 13 min read
Recall Stage in Recommendation Systems: From Intuition to Deep Learning
DataFunSummit
DataFunSummit
Dec 6, 2020 · Artificial Intelligence

E‑commerce Search Engine Recall and Vector Retrieval Techniques

This article explains how e‑commerce platforms use inverted indexes for fast word‑based recall, introduce vector‑based semantic retrieval, and combine deep‑learning models such as DSSM and DeepMatch with real‑time user behavior attention networks to generate efficient, personalized candidate sets for ranking.

Vector Retrievalrecall
0 likes · 15 min read
E‑commerce Search Engine Recall and Vector Retrieval Techniques
58UXD
58UXD
Sep 10, 2020 · Product Management

How a Fruit Store Story Reveals the Secrets of Search Recall and Precision

Using a fruit shop analogy, the article explains recall and precision metrics, illustrates their impact on recruitment search, and presents a matrix of design patterns—including cross‑database search, preset search sets, and matching labels—to boost both recall and accuracy while maintaining user experience.

Design PatternsUser experienceprecision
0 likes · 14 min read
How a Fruit Store Story Reveals the Secrets of Search Recall and Precision
DataFunTalk
DataFunTalk
Aug 23, 2020 · Artificial Intelligence

Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy

This article explains how Fliggy's travel recommendation platform tackles recall challenges such as cold‑start users, sparse behavior, itinerary‑specific needs, and periodic repurchase by applying user‑attribute models, graph embeddings, dual‑tower architectures, session‑based methods, and statistical repurchase forecasting to improve candidate selection and overall recommendation performance.

Travelcold startgraph embedding
0 likes · 16 min read
Optimizing Recall in Travel Recommendation Systems: Challenges and Solutions at Alibaba's Fliggy
JD Retail Technology
JD Retail Technology
Apr 7, 2020 · Artificial Intelligence

Fine-Grained Personalized Recommendation System Architecture for E-commerce

This article outlines the engineering architecture of a fine‑grained, personalized recommendation system for e‑commerce, covering core components such as feature data (offline and real‑time), algorithm engine (recall and ranking), technology choices like MongoDB, Elasticsearch, Kafka, Redis, and model deployment strategies.

algorithm enginee‑commercefeature data
0 likes · 9 min read
Fine-Grained Personalized Recommendation System Architecture for E-commerce
DataFunTalk
DataFunTalk
Mar 18, 2020 · Artificial Intelligence

Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu

This article presents a comprehensive technical overview of Meitu's personalized push notification pipeline, detailing the evolution of embedding methods (Word2Vec, Airbnb listing embedding, graph embedding), multiple recall strategies (global, personalized, attribute, and content‑based), and a progression of ranking models from logistic regression to field‑wise three‑tower architectures, highlighting their impact on click‑through rates.

AIDeep LearningPush Notification
0 likes · 12 min read
Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu
JD Tech Talk
JD Tech Talk
Mar 29, 2019 · Artificial Intelligence

Understanding Confusion Matrix, ROC Curve, and Evaluation Metrics for Binary Classification Models

After building a binary classification model, this article explains essential evaluation tools such as the confusion matrix, derived metrics like accuracy, precision, recall, F1 score, and the ROC curve, illustrating their definitions, visualizations, and practical considerations for different business scenarios.

Evaluation MetricsF1 scoreROC curve
0 likes · 6 min read
Understanding Confusion Matrix, ROC Curve, and Evaluation Metrics for Binary Classification Models
DataFunTalk
DataFunTalk
Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecommendation Systems
0 likes · 51 min read
Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems
Sohu Tech Products
Sohu Tech Products
Aug 29, 2018 · Artificial Intelligence

News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques

This article explains the architecture of news recommendation systems, detailing the two-stage recall and ranking process, various recall methods such as content‑based, collaborative filtering and matrix factorization, and advanced ranking models including LR, GBDT, FM, and wide‑and‑deep DNNs.

collaborative filteringmachine learningnews recommendation
0 likes · 14 min read
News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques
Meituan Technology Team
Meituan Technology Team
Jun 16, 2017 · Artificial Intelligence

Evolution of Meituan Travel Search Recall Strategies

Meituan‑Dianping’s travel search team tackles cross‑region queries and noisy data by iteratively refining a four‑step, case‑driven pipeline that classifies intent, segments queries, ranks results with distance and term‑importance models, and employs multi‑stage, parallel recall to steadily boost purchase rate, CTR, and user satisfaction.

SearchTravelintent classification
0 likes · 20 min read
Evolution of Meituan Travel Search Recall Strategies