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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.

advertisinghigh QPSlarge language model
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.comRanking
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.

Rankingadvertisinggraph neural network
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.

Modeladvertisingmachine learning
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.

Cold StartRankingRecommendation systems
0 likes · 14 min read
Comprehensive Overview of Recommendation System Technologies and Their Evolution
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.

RankingRecommendation systemsVector Retrieval
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.

Recommendation systemsVideo Streamingembedding
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.

AIRankingSearch Engine
0 likes · 23 min read
Design and Implementation of a Word Distribution Platform for Personalized Recommendations
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.

HNSWIndexingLarge Scale
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.

Cold StartRankingevaluation metrics
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.

Cold StartRankingRecommendation systems
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 IntelligenceRankingRecommendation systems
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 scorePrecisionclassification
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 DataRanking
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.

AIMulti-Path RetrievalRecommendation systems
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.

DBSCANEGESRanking
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.

AIRankinge‑commerce
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.

DBSCANEGESRanking
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.

BERTDSSMRanking
0 likes · 16 min read
Technical Evolution of the “Guess You Want” Recommendation Module in 58 Local Services