recommendation | BestHub

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

Apr 22, 2025 · Artificial Intelligence

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

JD’s advertising platform replaces rule‑based recall with a generative large‑model pipeline that unifies e‑commerce knowledge, multimodal user intent, and semantic IDs across recall, coarse‑ranking, fine‑ranking and creative optimization, while meeting sub‑100 ms latency and sub‑¥1‑per‑million‑token cost through quantization, parallelism, caching, and joint generative‑discriminative inference, delivering double‑digit performance gains and paving the way for domain‑specific foundation models.

Large Modelsadvertisingdistributed systems

0 likes · 20 min read

Generative Large‑Model Architecture for JD Advertising: Practices, Challenges, and Optimization

JD Tech

Apr 15, 2025 · Artificial Intelligence

Reliable Advertising Creative Generation and Personalized Recommendation via Multimodal Feedback and Offline Representation

The article presents a series of technical breakthroughs by JD's advertising team that improve the quality and coverage of AI‑generated ad images through a trustworthy multimodal feedback network, introduce a large human‑annotated image dataset, and enhance creative ranking with offline multimodal representations and online architecture optimizations, ultimately achieving more precise and scalable ad personalization.

AIAIGCadvertising

0 likes · 10 min read

Reliable Advertising Creative Generation and Personalized Recommendation via Multimodal Feedback and Offline Representation

Cognitive Technology Team

Mar 31, 2025 · Artificial Intelligence

Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching

Recommendation algorithms, rooted in machine learning and deep learning, transform massive user‑generated data into mathematical models that filter and personalize content, covering traditional collaborative filtering, matrix factorization, cosine similarity, and modern deep models such as Wide & Deep and Two‑Tower retrieval, illustrating their evolution and practical applications.

collaborative filteringdeep learningmachine learning

0 likes · 14 min read

Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching

DataFunSummit

Nov 22, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec’s recommendation system architecture, detailing training and inference optimizations, embedding parallelism, CPU/GPU placement strategies, online learning pipelines, and network compression techniques that together improve scalability, latency, and cost efficiency.

EasyRecTraining Optimizationdistributed systems

0 likes · 15 min read

EasyRec Recommendation Algorithm Training and Inference Optimization

Tencent Advertising Technology

Oct 17, 2024 · Artificial Intelligence

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

This article presents a comprehensive solution for heterogeneous long‑behavior sequence modeling in advertising recommendation, introducing the TIN backbone, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec, along with platform‑level optimizations that enable million‑scale sequences while delivering significant online performance gains.

Transformeradvertisingdeep learning

0 likes · 15 min read

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

JD Retail Technology

Oct 15, 2024 · Artificial Intelligence

Large‑Model‑Driven Evolution of E‑commerce Search and Recommendation at JD Retail

The article examines how large language models are reshaping JD Retail's e‑commerce search and recommendation pipelines, detailing industry evolution, technical challenges such as knowledge hallucination, intent understanding, personalization, cost, and safety, and presenting JD's end‑to‑end AIGC architecture, data preprocessing, alignment, evaluation, and next‑generation AI search solutions.

AILarge Modelse-commerce

0 likes · 36 min read

Large‑Model‑Driven Evolution of E‑commerce Search and Recommendation at JD Retail

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

DataFunTalk

Aug 26, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec's recommendation system architecture, detailing training and inference optimizations, distributed deployment strategies, operator fusion techniques, online learning pipelines, and network-level improvements to enhance performance and scalability.

AITraining Optimizationdistributed systems

0 likes · 15 min read

DataFunSummit

Aug 22, 2024 · Artificial Intelligence

Multimodal Algorithms for Content Understanding and Distribution in JD E‑commerce

This article presents JD's multimodal content‑understanding framework, detailing its five‑M business characteristics, the architecture of multimodal recall and ranking models, the GMF and MIN modules for semantic alignment and personalization, and future research directions involving large language models and end‑to‑end multimodal encoding.

AIRankingcontent understanding

0 likes · 16 min read

Multimodal Algorithms for Content Understanding and Distribution in JD E‑commerce

DataFunSummit

Aug 18, 2024 · Artificial Intelligence

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

The article examines the key challenges of recommendation AB testing at Xiaohongshu—including change stability, single‑experiment precision, and multi‑strategy packaging—and presents a series of engineering and statistical solutions such as SDK‑based AB architecture, virtual PreAA experiments, CUPED/DID adjustments, and reverse experiments to improve reliability and metric impact.

AB testingCUPEDPreAA

0 likes · 15 min read

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

DataFunTalk

Jul 31, 2024 · Artificial Intelligence

Decentralized Distribution in Xiaohongshu Recommendation System: Sideinfo, Multi‑modal Fusion, Interest Exploration and Future Directions

This article presents Xiaohongshu's technical solutions for decentralized content distribution, covering the definition of the problem, fast and accurate learning, side‑information modeling, graph‑based multi‑modal fusion, interest exploration and protection, and future research directions such as generative recommendation and large‑model driven user profiling.

Large Modelsdecentralized distributioninterest exploration

0 likes · 25 min read

Decentralized Distribution in Xiaohongshu Recommendation System: Sideinfo, Multi‑modal Fusion, Interest Exploration and Future Directions

Tencent Advertising Technology

Jul 17, 2024 · Artificial Intelligence

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

This article summarizes Tencent Advertising's recent research on recommendation models, covering comprehensive feature encoding techniques, solutions to embedding dimensional collapse through multi‑embedding paradigms, and novel methods such as STEM and AME to disentangle conflicting user interests across multiple tasks.

dimensional collapseembeddingfeature encoding

0 likes · 20 min read

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

DaTaobao Tech

Jun 19, 2024 · Product Management

Multi‑Interest Vector Recall and PDN Models for Large‑Asset Recommendation in Alibaba Auction

Alibaba Auction improves large‑asset recommendation by deploying the multi‑interest vector recall model MIND and the two‑hop PDN model, adapting features and time weighting for unique, high‑value items, using hard‑negative sampling and combined rule‑based and vector similarity, which boosts conversion metrics while revealing filter‑bubble concerns.

PDNdeep learninge-commerce

0 likes · 13 min read

Multi‑Interest Vector Recall and PDN Models for Large‑Asset Recommendation in Alibaba Auction

Python Programming Learning Circle

Jun 14, 2024 · Backend Development

Student Training Plan Management System Using Python, Flask, and MySQL

This article introduces a Python‑Flask‑MySQL student training plan management system, detailing its features such as visualized progress, SVD‑based course recommendation, forum discussion, simulated enrollment, project structure, environment setup, step‑by‑step installation, configuration, and usage instructions.

FlaskMySQLPython

0 likes · 3 min read

Student Training Plan Management System Using Python, Flask, and MySQL

DataFunSummit

Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

GNNeBayembedding

0 likes · 19 min read

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

DataFunSummit

Jun 2, 2024 · Artificial Intelligence

Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases

This article presents a comprehensive overview of building a user profile tag system—including tag taxonomy, platform architecture, construction methods, update cycles, access patterns, common algorithmic tags, and real‑world applications such as marketing, metric attribution, and A/B testing—illustrated with examples and a detailed Q&A session from a data‑mining senior manager at Qunar.

AB testingData MiningTag System

0 likes · 21 min read

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

Airbnb Technology Team

Apr 15, 2024 · Artificial Intelligence

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

Airbnb’s Attribute Prioritization System uses a machine‑learning pipeline called LATEX to extract and map guest‑mentioned amenities, activities and places from reviews, messages and tickets, then predicts and ranks the most important attributes per listing, giving hosts personalized suggestions to improve listings and match traveler needs.

AirbnbNERNLP

0 likes · 9 min read

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

Kuaishou Tech

Mar 8, 2024 · Artificial Intelligence

Three Selected Papers from WSDM 2024 on Recommendation Systems

This article highlights three oral papers accepted at WSDM 2024 that address cross‑domain sequential recommendation, extremely sparse feedback denoising recommendation, and automated label crafting for short‑video recommendation, providing their abstracts, author lists, and links to PDFs and source code.

AICross-DomainWSDM2024

0 likes · 7 min read

Three Selected Papers from WSDM 2024 on Recommendation Systems