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personalization

170 articles · Page 1 of 2
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 3, 2026 · Artificial Intelligence

MemSlides: Memory‑Driven Slides Agent for Multi‑Round Precise PPT Edits (HuggingFace #1)

MemSlides introduces a hierarchical memory framework—persistent user profiles, active working memory, and tool experience—to turn AI slide generation into a stateful multi‑turn authoring process, improving persona alignment and local edit efficiency, with closed‑loop completion rising from 0.815 to 0.963 and edit time dropping from 609.5 s to 242.5 s.

AI PPT generationMemSlideslocal modification
0 likes · 13 min read
MemSlides: Memory‑Driven Slides Agent for Multi‑Round Precise PPT Edits (HuggingFace #1)
Machine Heart
Machine Heart
Jun 25, 2026 · Artificial Intelligence

From Finding to Generating Videos: How Kuaishou’s RaG Transforms Recommendation Systems

Kuaishou’s new Recommendation-as-Generation (RaG) framework replaces traditional retrieve-and-rank with a generative pipeline that predicts user interests, creates personalized video content, and closes the loop with feedback, delivering a 1.87% ad‑revenue lift for over 400 million daily users.

A/B testingGenerative AILarge-Scale Deployment
0 likes · 14 min read
From Finding to Generating Videos: How Kuaishou’s RaG Transforms Recommendation Systems
DataFunTalk
DataFunTalk
Jun 7, 2026 · Artificial Intelligence

ChatGPT’s Dreaming V3 Memory Upgrade: Free for a Billion Users

OpenAI unveiled Dreaming V3, a new memory architecture that lets ChatGPT silently replay and consolidate daily conversations, achieving 82.8% context recall, 71.3% preference compliance, five‑fold compute savings, and free access for billions while offering a transparent memory‑summary interface.

AI memoryChatGPTDreaming V3
0 likes · 9 min read
ChatGPT’s Dreaming V3 Memory Upgrade: Free for a Billion Users
AI Step-by-Step
AI Step-by-Step
Jun 5, 2026 · Artificial Intelligence

The Hidden Engine Powering OpenClaw: Inside Pi Agent

OpenClaw’s rapid rise as a personal AI gateway is driven by its core engine Pi Agent, which offers layered execution modes, strict security controls, filesystem‑based personalization, lightweight extensions, and an RL‑based personalization loop for continuous agent improvement.

AI agentsOpenClawPi agent
0 likes · 5 min read
The Hidden Engine Powering OpenClaw: Inside Pi Agent
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 24, 2026 · Artificial Intelligence

Can Agents Have Their Own App Store? SJTU & OPPO Unveil a Massive Agent Ecosystem

The article analyzes the ColorEcosystem blueprint, which maps the evolution from single LLM‑driven agents to a massive, personalized, standardized, and trustworthy agent ecosystem, detailing its three pillars—Agent Carrier, Agent Store, and Agent Audit—along with challenges and transition strategies.

AI agentsLarge Language Modelsagent audit
0 likes · 12 min read
Can Agents Have Their Own App Store? SJTU & OPPO Unveil a Massive Agent Ecosystem
SuanNi
SuanNi
May 7, 2026 · Artificial Intelligence

GPT-5.5 Instant Cuts Hallucinations by 52.5% and Delivers More Concise Answers

OpenAI's free GPT-5.5 Instant replaces GPT-5.3 as the default model, slashing hallucinations by 52.5% in high‑risk domains, improving factual accuracy, providing shorter yet precise responses, adding memory‑controlled personalization, and rolling out to all ChatGPT users via the chat‑latest API.

AIGPT-5.5OpenAI
0 likes · 6 min read
GPT-5.5 Instant Cuts Hallucinations by 52.5% and Delivers More Concise Answers
Old Zhang's AI Learning
Old Zhang's AI Learning
May 6, 2026 · Artificial Intelligence

GPT-5.5 Instant Arrives: Smarter, Clearer, More Personalized AI

OpenAI has silently replaced the default ChatGPT model with GPT‑5.5 Instant, delivering a 52.5% drop in hallucinations, 30% shorter responses, deeper personalization via memory sources, and higher benchmark scores across a range of professional tasks, while rolling out new pricing and usage tiers.

AI benchmarksChatGPTGPT-5.5
0 likes · 11 min read
GPT-5.5 Instant Arrives: Smarter, Clearer, More Personalized AI
Tencent Cloud Developer
Tencent Cloud Developer
Apr 21, 2026 · Artificial Intelligence

Why Hermes Overtook OpenClaw: A Deep Dive into AI Agent Evolution and Market Impact

The article analyzes Hermes' explosive seven‑week rise, its writable runtime that learns and self‑optimizes, and why it outperformed the previously dominant OpenClaw by comparing growth metrics, technical architectures, token‑consumption ROI, market positioning, and practical use‑case recommendations for developers and enterprises.

AI agentsHermesMarket Analysis
0 likes · 26 min read
Why Hermes Overtook OpenClaw: A Deep Dive into AI Agent Evolution and Market Impact
Design Hub
Design Hub
Mar 19, 2026 · Artificial Intelligence

Midjourney V8 Alpha: From prettier pictures to an image operating system

Midjourney V8 Alpha introduces faster 2K rendering, stronger prompt understanding, and new workflow features like personalization, moodboard, and conversation mode, shifting the tool from a high‑quality image generator to a controllable image operating system, though at higher cost and complexity.

AI image generationMidjourneyV8 Alpha
0 likes · 15 min read
Midjourney V8 Alpha: From prettier pictures to an image operating system
Qborfy AI
Qborfy AI
Mar 4, 2026 · Artificial Intelligence

Build a Personal AI Prompt That Saves You Time Every Session

The article explains how to create a personal AI prompt—called a Master Prompt or Personal OS—by documenting identity, goals, style preferences, and using it each session, with step‑by‑step guidance, examples, and tips for automating the creation and adapting it to multiple roles.

AI Prompt EngineeringAI workflowChatGPT
0 likes · 8 min read
Build a Personal AI Prompt That Saves You Time Every Session
Data Party THU
Data Party THU
Feb 24, 2026 · Artificial Intelligence

Why Long Contexts Undermine LLM Reliability: Hidden Risks of Personalization and Shared Sessions

The article analyzes how expanding the context window of large language models creates scarce attention, introduces unreproducible personalization, mixes intents in shared accounts, and leads to performance degradation, making debugging, testing, and reliable production deployment increasingly difficult.

AI ReliabilityContext Managementpersonalization
0 likes · 11 min read
Why Long Contexts Undermine LLM Reliability: Hidden Risks of Personalization and Shared Sessions
JD Retail Technology
JD Retail Technology
Nov 4, 2025 · Artificial Intelligence

How AIGC Is Transforming E‑commerce with Personalized Visual Content

This article explains how large‑model AIGC technology reshapes e‑commerce by enabling mass‑produced, user‑profile‑driven visual assets, detailing the evolution from early online trade to the 2.0 era, the technical pipeline of multimodal models, and the practical impact on merchants.

AIGCLarge Language ModelsMultimodal AI
0 likes · 17 min read
How AIGC Is Transforming E‑commerce with Personalized Visual Content
Code Mala Tang
Code Mala Tang
Sep 27, 2025 · Artificial Intelligence

What Is ChatGPT Pulse and How It Redefines AI Assistance

ChatGPT Pulse, a new feature for ChatGPT Pro users, shifts AI interaction from passive Q&A to proactive, personalized assistance by analyzing past chats, user preferences, and integrated apps, delivering daily asynchronous updates that aim to reduce information overload while learning from user feedback.

AI assistantsChatGPT PulseOpenAI
0 likes · 8 min read
What Is ChatGPT Pulse and How It Redefines AI Assistance
DataFunTalk
DataFunTalk
Sep 26, 2025 · Artificial Intelligence

How ChatGPT’s New “Pulse” Turns AI Into a Proactive Personal Assistant

OpenAI unveiled the ChatGPT “Pulse” preview, a new agent‑based feature that nightly researches users’ chats, feedback and calendar data to deliver personalized, proactive updates each morning, allowing Pro users to link Gmail and Google Calendar, manage research topics, and give feedback for continual improvement.

ChatGPTOpenAIPULSE
0 likes · 6 min read
How ChatGPT’s New “Pulse” Turns AI Into a Proactive Personal Assistant
DataFunSummit
DataFunSummit
Nov 6, 2024 · Artificial Intelligence

Applying AIGC to Transform Insurance Marketing at Ant Group

This article explains how Ant Group’s insurance marketing team leverages Artificial Intelligence‑generated content (AIGC) to create personalized marketing materials, automate recommendation workflows, and produce video scripts, thereby improving efficiency, compliance, and user engagement in the insurance sector.

AIGCArtificial IntelligenceContent Generation
0 likes · 9 min read
Applying AIGC to Transform Insurance Marketing at Ant Group
NewBeeNLP
NewBeeNLP
Jul 22, 2024 · Artificial Intelligence

How Meta Scales User Modeling for Ads: Inside the SUM Framework

This article examines Meta's SUM (Scaling User Modeling) system, detailing its upstream‑downstream architecture, the SOAP online asynchronous serving platform, production optimizations, and extensive offline and online experiments that demonstrate significant gains in ad personalization performance.

Deep LearningMetaRecommendation Systems
0 likes · 19 min read
How Meta Scales User Modeling for Ads: Inside the SUM Framework
DataFunSummit
DataFunSummit
Jun 26, 2024 · Artificial Intelligence

2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration

This article outlines the current bottlenecks of conventional recommendation pipelines and proposes a comprehensive 2026 research agenda covering retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value modeling, whole‑site optimization, interactive recommendation, personalized modeling, decision‑theoretic formulation, and the OneRec multi‑source fusion framework.

Large Language ModelsUser Retentionmulti-objective optimization
0 likes · 18 min read
2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration
DataFunSummit
DataFunSummit
Jun 23, 2024 · Artificial Intelligence

Tongyi Xingchen Personalized Large Model: Technical Overview and Applications

This article summarizes the development background of large language models, Alibaba's progression in foundational and personalized AI, the design and capabilities of the Tongyi Xingchen personalized model, its multimodal and agent-based architecture, various industry use cases, and the safety and responsibility measures applied to ensure trustworthy AI deployment.

AI safetyLarge Language ModelsMultimodal AI
0 likes · 13 min read
Tongyi Xingchen Personalized Large Model: Technical Overview and Applications
JD Tech
JD Tech
Jun 23, 2024 · Artificial Intelligence

Applying Large Models to Recommendation Systems: Strategies, Challenges, and E‑commerce Case Study

This article examines how large pre‑trained models such as GPT‑4 and BERT are integrated into modern recommendation systems, detailing their advantages, implementation strategies, real‑world e‑commerce case studies, and the technical and privacy challenges that must be addressed for effective deployment.

Artificial Intelligencelarge modelsmodel compression
0 likes · 14 min read
Applying Large Models to Recommendation Systems: Strategies, Challenges, and E‑commerce Case Study
NewBeeNLP
NewBeeNLP
Feb 2, 2024 · Artificial Intelligence

ControlRec: Aligning LLMs with IDs to Boost Personalized Recommendations

ControlRec introduces heterogeneous feature matching and instruction contrastive learning to bridge the semantic gap between language models and discrete user/item IDs, enabling more effective personalized recommendation across multiple tasks such as rating prediction, sequential recommendation, and explanation generation.

ControlRecHeterogeneous Feature MatchingInstruction Contrast Learning
0 likes · 10 min read
ControlRec: Aligning LLMs with IDs to Boost Personalized Recommendations
DataFunSummit
DataFunSummit
Jan 23, 2024 · Artificial Intelligence

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

This article presents Tencent TRS's industrial practice of applying meta‑learning and cross‑domain recommendation to address personalization challenges, detailing problem definitions, solution architectures, algorithmic choices such as MAML, deployment strategies, and the cost‑effective outcomes achieved across multiple scenarios.

MAMLMeta LearningRecommendation Systems
0 likes · 16 min read
Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS
Baidu Geek Talk
Baidu Geek Talk
Jan 17, 2024 · Industry Insights

How Baidu Boosted Search Push Clicks with Model Calibration and DeltaCTR Strategies

This article details Baidu Search's personalized push system, covering challenges in material selection and user targeting, the end‑to‑end workflow, model accuracy improvements, pCTR calibration techniques, deltaCTR‑based ranking, and the combined offline‑online experiments that significantly raised both CTR and DAU.

BaiduCTR optimizationdeltaCTR
0 likes · 16 min read
How Baidu Boosted Search Push Clicks with Model Calibration and DeltaCTR Strategies
58UXD
58UXD
Dec 12, 2023 · Artificial Intelligence

How Brands Use AI-Powered Memes to Captivate Gen Z: 3 Real-World Cases

Exploring how AI reshapes brand-consumer interaction, this article examines Generation Z's cultural preferences and showcases three real-world cases—The North Face, McDonald's, and Wei Long—demonstrating AI-driven meme marketing that blends personalization, cultural trends, and immersive storytelling to boost engagement.

AI marketingGen ZMeme Culture
0 likes · 7 min read
How Brands Use AI-Powered Memes to Captivate Gen Z: 3 Real-World Cases
Baidu MEUX
Baidu MEUX
Dec 6, 2023 · Product Management

Baidu Search’s Personalized Gaokao Experience: Design, Usability, and Growth

This case study details how Baidu Search transformed its Gaokao services by introducing a full‑process personalized companion module, an intuitive volunteer‑selection tool, and an efficient information architecture, resulting in higher user engagement, improved satisfaction, and notable growth in the 2023 exam season.

Baidu SearchGaokaoProduct design
0 likes · 10 min read
Baidu Search’s Personalized Gaokao Experience: Design, Usability, and Growth
DataFunTalk
DataFunTalk
Nov 15, 2023 · Artificial Intelligence

Contextual Learning for Personalized Text‑to‑Image Generation

This article explains how contextual learning can enhance text‑to‑image models by incorporating example image‑text pairs, redesigning the UNet architecture, building large in‑context training datasets, and training the SuTI model to achieve fast, controllable, and high‑quality personalized image generation.

AIDiffusion Modelscontextual learning
0 likes · 24 min read
Contextual Learning for Personalized Text‑to‑Image Generation
php Courses
php Courses
Sep 29, 2023 · Backend Development

Using PHP Sessions and Cookies to Enhance Web Applications

This article explains how PHP sessions and cookies work together to manage user data, improve authentication, shopping carts, and personalization, providing code examples and best practices for secure, seamless web application development.

PHPSessionsShopping Cart
0 likes · 7 min read
Using PHP Sessions and Cookies to Enhance Web Applications
Alimama Tech
Alimama Tech
Aug 16, 2023 · Artificial Intelligence

Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising

PerBid introduces a personalized automated bidding framework that creates context‑aware RL agents for advertiser clusters using a profiling network to embed static and dynamic campaign features, and experiments on Alibaba’s display‑ad platform show up to 10.85% performance gains while markedly improving fairness across heterogeneous advertisers.

Online Advertisingautomated biddingfairness
0 likes · 23 min read
Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising
JD Retail Technology
JD Retail Technology
Aug 15, 2023 · Artificial Intelligence

Design and Implementation of a Recommendation Algorithm PaaS for Scalable Business Scenarios

This document describes the background, design, capability classification, implementation details, case studies, practical experience, and future outlook of a recommendation‑algorithm Platform‑as‑a‑Service (PaaS) that enables reusable, extensible, and configurable recommendation capabilities across dozens of business lines.

PaaSPlatformalgorithm
0 likes · 18 min read
Design and Implementation of a Recommendation Algorithm PaaS for Scalable Business Scenarios
Kuaishou Tech
Kuaishou Tech
Aug 11, 2023 · Artificial Intelligence

PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation

The paper introduces PEPNet, a plug‑and‑play network that tackles the domain‑seesaw and task‑seesaw problems in multi‑scenario recommendation by using a gated personalization module (GateNU) together with embedding‑level (EPNet) and parameter‑level (PPNet) personalization, and demonstrates its superiority through extensive offline and online experiments on Kuaishou data.

Deep LearningEmbeddingMulti-Task Learning
0 likes · 11 min read
PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation
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 Systemsmachine learning
0 likes · 10 min read
Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations
JD Cloud Developers
JD Cloud Developers
Aug 1, 2023 · Artificial Intelligence

How PaaS Revolutionizes Recommendation Algorithms for Scalable Business Impact

This article details the design, componentization, platformization, and low‑code tools of a recommendation‑algorithm PaaS that streamlines development, supports diverse business scenarios, and accelerates delivery of personalized recommendation capabilities across multiple product lines.

Low-Code DevelopmentPaaSPlatform Engineering
0 likes · 16 min read
How PaaS Revolutionizes Recommendation Algorithms for Scalable Business Impact
SQB Blog
SQB Blog
Jul 20, 2023 · Artificial Intelligence

How We Built and Optimized a Multi‑Pool Recommendation System for Boss Circle

This article explains the design, implementation, and iterative optimization of Boss Circle's recommendation engine, covering the initial simple ranking, the introduction of Elasticsearch‑based scoring, multi‑pool data sources, machine‑learning experiments, real‑time feature handling, and future personalization challenges.

ElasticsearchRankingdata pipelines
0 likes · 17 min read
How We Built and Optimized a Multi‑Pool Recommendation System for Boss Circle
DataFunTalk
DataFunTalk
Jul 6, 2023 · Artificial Intelligence

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

This article presents Tencent TRS's industrial deployment of meta‑learning and cross‑domain recommendation, detailing problem definitions, solution architectures, challenges of industrialization, and practical implementations that achieve personalized modeling and cost‑effective multi‑scene recommendation across various online services.

MAMLRecommendation Systemscross-domain
0 likes · 18 min read
Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS
DataFunSummit
DataFunSummit
Jun 30, 2023 · Artificial Intelligence

Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions

In this expert roundtable, leading researchers and engineers discuss the current state of recommendation systems, how large language models can reshape the field, the technical and practical challenges involved, and practical advice for practitioners looking to adopt AI‑driven personalization solutions.

AILarge Language ModelsRecommendation Systems
0 likes · 36 min read
Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions
DataFunTalk
DataFunTalk
Jun 20, 2023 · Artificial Intelligence

How Recommendation Systems Work and Their Integration with ChatGPT

This article explains the fundamentals of recommendation systems, their digital representation, how ChatGPT and large language models are applied to enhance recommendation performance, and highlights emerging trends such as conversational recommendation and a recommended book on the subject.

AIChatGPTConversational AI
0 likes · 8 min read
How Recommendation Systems Work and Their Integration with ChatGPT
58UXD
58UXD
Apr 20, 2023 · Artificial Intelligence

How AI is Shaping Emotional Experience Design—and Its Hidden Limits

This article explores how artificial intelligence enhances emotional experience design through emotion recognition, personalized services, and sustainable applications, while also highlighting technical, cultural, and privacy challenges and offering practical recommendations for designers to create more empathetic and responsible AI‑driven experiences.

AIemotional designpersonalization
0 likes · 7 min read
How AI is Shaping Emotional Experience Design—and Its Hidden Limits
WeChat Game Design
WeChat Game Design
Mar 28, 2023 · Game Development

How We Turned Game Data into a Shareable Story: The 2022 WeChat Game Year‑End Review

This case study details how the WeChat Games team designed a 2022 year‑end review that transforms player statistics into an engaging, story‑driven experience by leveraging emotional storytelling, personalized personas, comic‑style visuals, and clever easter eggs to boost shareability and user delight.

Data StorytellingWeChat gamespersonalization
0 likes · 8 min read
How We Turned Game Data into a Shareable Story: The 2022 WeChat Game Year‑End Review
DataFunTalk
DataFunTalk
Mar 13, 2023 · Big Data

Building and Operating a User Portrait Platform: Architecture, Practices, and Case Studies from Kuakan Comics

This article presents a comprehensive overview of Kuakan Comics' user portrait platform, detailing its product architecture, data‑warehouse modeling, device‑ID strategies, multi‑business tag integration, composite tag support, real‑world applications, and future directions for large‑scale data‑driven personalization.

Tag Managementcross‑business integrationdevice ID
0 likes · 32 min read
Building and Operating a User Portrait Platform: Architecture, Practices, and Case Studies from Kuakan Comics
DataFunTalk
DataFunTalk
Feb 1, 2023 · Artificial Intelligence

Kuaishou Recommendation System: Architecture, CTR Modeling, Multi‑Domain Multi‑Task Learning, and Long‑Short Term Behavior Modeling

This article presents a comprehensive overview of Kuaishou's large‑scale recommendation system, detailing its pipeline, unique characteristics, CTR model improvements, the PPNet personalization network, multi‑domain multi‑task framework, short‑ and long‑term behavior sequence modeling, and the challenges of handling billions of features and trillions of parameters.

AICTR modelMulti-Task Learning
0 likes · 12 min read
Kuaishou Recommendation System: Architecture, CTR Modeling, Multi‑Domain Multi‑Task Learning, and Long‑Short Term Behavior Modeling
DataFunSummit
DataFunSummit
Jan 29, 2023 · Big Data

User Profiling: Development Process and Applications in Data Intelligence

The article explains how user profiling—labeling users' habits, behaviors, and attributes—serves as the foundation for big‑data‑driven personalized recommendation, advertising, and intelligent marketing, and outlines a step‑by‑step development workflow from tag design to service deployment.

Data IntelligenceMarketing AnalyticsTagging
0 likes · 5 min read
User Profiling: Development Process and Applications in Data Intelligence
DataFunTalk
DataFunTalk
Jan 27, 2023 · Big Data

User Profiling: Development Process and Applications in Data Intelligence

The article explains how user profiling—labeling user habits, behaviors, and attributes—has become essential for refined user operations, outlines a step‑by‑step development workflow, and highlights its role in personalized recommendation, precise advertising, and intelligent marketing within the big‑data ecosystem.

Taggingdata analyticspersonalization
0 likes · 4 min read
User Profiling: Development Process and Applications in Data Intelligence
DaTaobao Tech
DaTaobao Tech
Jan 9, 2023 · Artificial Intelligence

Adaptive and Self-Supervised Multi-Scenario Modeling for Taobao Personalized Recommendation

On January 9 from 19:00 to 20:00, algorithm engineer Zhang Yuanliang will present Taobao’s scenario-adaptive, self-supervised multi-scenario recommendation model, detailing its architecture, experimental results, and practical deployment for improving personalized item recall across diverse user contexts.

algorithmmulti-scenariopersonalization
0 likes · 1 min read
Adaptive and Self-Supervised Multi-Scenario Modeling for Taobao Personalized Recommendation
DataFunSummit
DataFunSummit
Nov 30, 2022 · Artificial Intelligence

Combining Knowledge Graphs with Personalized News Recommendation Systems

This article presents a comprehensive overview of a personalized news recommendation system that leverages knowledge graphs to improve accuracy, explainability, and user satisfaction, detailing background motivations, graph construction methods, model architecture, experimental results, and practical insights from a Meituan research perspective.

Deep LearningGraph Neural Networksexplainability
0 likes · 23 min read
Combining Knowledge Graphs with Personalized News Recommendation Systems
DataFunTalk
DataFunTalk
Sep 25, 2022 · Artificial Intelligence

Personalized News Recommendation System Based on Knowledge Graphs

This talk presents a personalized news recommendation system that leverages knowledge graphs to enhance recommendation accuracy, explainability, and user interest modeling, detailing background, graph construction methods, multi‑task deep learning architecture, experimental results, and future research directions.

Deep LearningGraph Constructionexplainability
0 likes · 22 min read
Personalized News Recommendation System Based on Knowledge Graphs
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Jun 30, 2022 · Artificial Intelligence

Personalized Recommendation of Game Cosmetic Items: From Popularity to Latent Factor Models

The article explores how to recommend visually appealing game cosmetics—such as character outfits and weapon skins—by transforming subjective notions of beauty into objective features using popularity heuristics, tag‑based labeling, and latent factor models to predict player preferences.

Tagginggame cosmeticslatent factor model
0 likes · 8 min read
Personalized Recommendation of Game Cosmetic Items: From Popularity to Latent Factor Models
DataFunTalk
DataFunTalk
May 7, 2022 · Artificial Intelligence

Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact

This article explains how JD's intelligent recommendation selling point system leverages NLP, BERT, Transformer and pointer‑generator models to automatically create short, personalized product highlights, describing the technical background, system architecture, model training pipeline, online/offline monitoring, and the resulting business benefits.

BERTNLPRecommendation Systems
0 likes · 13 min read
Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact
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
Top Architect
Top Architect
Dec 6, 2021 · Backend Development

Design and Evolution of Baidu Short‑Video Push System

This article details the architecture, data flow, module responsibilities, and successive optimizations of Baidu's short‑video push system, covering personalized timing estimation, user‑group management, frequency‑control redesign, and protobuf‑based compression to handle billion‑scale traffic efficiently.

Notificationarchitecturedistributed system
0 likes · 16 min read
Design and Evolution of Baidu Short‑Video Push System
Architect
Architect
Dec 5, 2021 · Backend Development

Design and Optimization of Baidu Short Video Push System

This article presents a comprehensive overview of Baidu's short‑video Push system, detailing its architecture, core modules, data flows, and successive optimizations such as personalized send‑time estimation, user‑group management, frequency‑control redesign, and protobuf compression, illustrating how these improvements enhance scalability, reliability, and resource efficiency.

Push Systemdistributed architecturefrequency control
0 likes · 15 min read
Design and Optimization of Baidu Short Video Push System
Big Data Technology & Architecture
Big Data Technology & Architecture
Dec 1, 2021 · Big Data

Building a Complete Retail Industry Tagging System for Data‑Driven Operations

This article explains how D‑E‑Commerce designed and implemented a comprehensive retail‑industry tag taxonomy and data‑asset framework to enable data‑driven operations and personalized recommendations, detailing the architecture, backend and frontend tag structures, and their practical application in marketing and analytics.

Retaildata taggingpersonalization
0 likes · 7 min read
Building a Complete Retail Industry Tagging System for Data‑Driven Operations
DataFunSummit
DataFunSummit
Nov 15, 2021 · Artificial Intelligence

Hotel Search Relevance Construction and Modeling at Fliggy (Alibaba)

This article presents a comprehensive overview of Fliggy's hotel search system, covering its multi‑platform background, architecture, complex relevance factors—including text, spatial, and price—and the modeling techniques used to fuse these signals for personalized ranking, along with future improvement directions.

AIRankinghotel search
0 likes · 18 min read
Hotel Search Relevance Construction and Modeling at Fliggy (Alibaba)
DataFunSummit
DataFunSummit
Nov 7, 2021 · Artificial Intelligence

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

The article examines how low‑level recommendation‑algorithm improvements in information‑flow feeds can boost user retention, LTV and overall growth by addressing cold‑start challenges, survivor bias, and causal inference through personalized ranking, ecosystem construction, and multi‑task learning.

Information Flowalgorithmcausal inference
0 likes · 14 min read
How Information‑Flow Recommendation Systems Upgrade Drives User Growth
DataFunSummit
DataFunSummit
Aug 3, 2021 · Artificial Intelligence

Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent

The article explains how Tencent addresses the limitations of traditional content understanding methods in personalized recommendation by introducing an interest‑graph framework that combines classification, concept, entity, and event layers, and details the associated mining, matching, and online evaluation techniques.

EmbeddingNLPcontent understanding
0 likes · 13 min read
Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent
Alimama Tech
Alimama Tech
Jul 14, 2021 · Backend Development

Real-Time Image Rendering Service for Personalized Advertising Using Rust

To eliminate wasteful pre‑generated ad creatives, a Taobao team built a high‑concurrency, Rust‑based real‑time image rendering service that safely composes personalized templates on the fly, delivering thousands of requests per second with millisecond latency, powering diverse advertising scenarios and achieving roughly a 36 % business lift.

High concurrencyRustimage service
0 likes · 9 min read
Real-Time Image Rendering Service for Personalized Advertising Using Rust
DataFunTalk
DataFunTalk
Jun 15, 2021 · Artificial Intelligence

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

The paper introduces PAPERec, a personalized multi‑objective recommendation framework that leverages Pareto‑oriented reinforcement learning to generate user‑specific objective weights, enabling the model to approximate Pareto‑optimal solutions and achieve superior click‑through rate and dwell‑time performance in both offline and online experiments.

CTRPareto efficiencyRecommendation Systems
0 likes · 12 min read
Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization
DataFunTalk
DataFunTalk
Apr 17, 2021 · Artificial Intelligence

Personalized Re-ranking for Recommendation (ResSys'19)

This article introduces a personalized re‑ranking model for recommendation systems, explaining the limitations of traditional point‑wise ranking, describing the PRM architecture with input, encoding, and output layers using multi‑head attention and pre‑trained personalization features, and presenting experimental results and future extensions.

CTRRe‑rankingTransformer
0 likes · 7 min read
Personalized Re-ranking for Recommendation (ResSys'19)
vivo Internet Technology
vivo Internet Technology
Mar 17, 2021 · Artificial Intelligence

Design and Architecture of the Vivo App Store Recommendation System

The Vivo App Store recommendation system uses a modular, hot‑plug architecture—layered from plugins to base services and employing Template Method, Strategy, and Composite patterns—to integrate unchanged data sources, deliver high‑performance personalized recommendations, reduce development effort by ~75%, and enable rapid, low‑bug scene‑specific upgrades.

Software engineeringbackend-architecturepersonalization
0 likes · 12 min read
Design and Architecture of the Vivo App Store Recommendation System
DataFunTalk
DataFunTalk
Mar 4, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article presents the design and implementation of interactive recommendation and travel‑theme recommendation in Alibaba's Fliggy app, covering background, user demand classification, real‑time interest capture, various recall strategies, ranking models, multi‑task learning, and engineering tricks to improve CTR and user experience.

AIFliggyinteractive recommendation
0 likes · 16 min read
Interactive Recommendation and Travel Theme Recommendation in the Fliggy App
DevOps
DevOps
Mar 4, 2021 · Product Management

Understanding the Customer Journey in Internet Consumer Finance

The article explains how digital transformation reshapes the customer journey in consumer finance, highlighting its definition, four key capabilities—automation, personalization, contextual interaction, and journey innovation—and outlining stages from acquisition to advocacy to guide firms in building comprehensive, data‑driven experiences.

AutomationProduct Managementconsumer finance
0 likes · 10 min read
Understanding the Customer Journey in Internet Consumer Finance
DataFunTalk
DataFunTalk
Feb 25, 2021 · Artificial Intelligence

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

This article describes how a UGC app tackled user and content cold‑start problems by introducing a personalized vector‑recall pipeline based on network representation learning and multimodal embeddings, detailing graph construction, GraphSAGE and GAT implementations, offline experiments, A/B test results, and future directions.

GNNMultimodalgraph-embedding
0 likes · 14 min read
Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community
Sohu Tech Products
Sohu Tech Products
Feb 24, 2021 · Artificial Intelligence

EdgeRec: Edge Computing in Recommendation Systems

EdgeRec explores how moving recommendation system components to the edge—leveraging real‑time user behavior, heterogeneous action modeling, on‑device reranking, mixed‑ranking, and personalized “thousand‑person‑one‑model” training—can reduce latency, improve relevance, and boost business metrics compared to traditional cloud‑centric pipelines.

Meta LearningRecommendation Systemsedge computing
0 likes · 19 min read
EdgeRec: Edge Computing in Recommendation Systems
Alibaba Terminal Technology
Alibaba Terminal Technology
Feb 5, 2021 · Frontend Development

How Intelligent UI Boosted Alibaba’s Holiday Sales by 10%+ Through User Preference Modeling

This article explains how Alibaba’s CBU team tackled decision overload by building an intelligent UI that uses user‑behavior and product‑preference models, replaces algorithmic cold‑start, reduces reliance on traffic, and delivers over 10% PVctr growth across multiple holiday campaigns through systematic tagging, low‑code material development, and rigorous A/B experimentation.

A/B testingAlgorithmic RecommendationUser Preference Modeling
0 likes · 20 min read
How Intelligent UI Boosted Alibaba’s Holiday Sales by 10%+ Through User Preference Modeling
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
DataFunTalk
DataFunTalk
Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNDeep LearningGBDT
0 likes · 17 min read
Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR
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
DataFunTalk
DataFunTalk
Jan 8, 2021 · Artificial Intelligence

Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies

This article provides a comprehensive overview of e‑commerce recommendation systems, detailing their end‑to‑end workflow, key challenges such as multi‑scenario objectives and data loops, core components like recall and ranking, model evolution, feature engineering, evaluation metrics, and practical considerations for building a healthy, multi‑objective recommendation ecosystem.

Rankinge-commercemachine learning
0 likes · 17 min read
Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies
58UXD
58UXD
Dec 21, 2020 · Product Management

How 58.com Revamped Its Job Search Page with Personalized Recommendations

This case study details 58.com's full‑time recruitment page redesign, shifting from group‑based to individual‑focused personalization, introducing three recommendation zones, a feedback loop, and inclusive, fun UI elements to boost user engagement and application rates.

Product ManagementUX designblue-collar
0 likes · 8 min read
How 58.com Revamped Its Job Search Page with Personalized Recommendations
DataFunTalk
DataFunTalk
Dec 2, 2020 · Artificial Intelligence

How Recommendation Algorithms Drive User Growth in Content Feed Systems

This article examines how low‑level recommendation algorithm techniques can upgrade content‑feed systems to boost user growth, covering problem analysis, growth factors, personalization upgrades, cold‑start mechanisms, bias mitigation via causal inference, and utility‑driven user profiling.

Recommendation Systemsalgorithm designcausal inference
0 likes · 14 min read
How Recommendation Algorithms Drive User Growth in Content Feed Systems
JD Cloud Developers
JD Cloud Developers
Nov 4, 2020 · Artificial Intelligence

How Cloud Trade Fairs Use AI to Power Smart Recommendations

This article explains how a cloud‑based trade fair leverages AI techniques—including user and item profiling, multi‑level caching with Caffeine and Redis, and a Deep Interest Network model with attention mechanisms—to deliver personalized, high‑performance recommendations for exhibitors, buyers, and individual users.

AICachingDeep Learning
0 likes · 15 min read
How Cloud Trade Fairs Use AI to Power Smart Recommendations
Yuewen Technology
Yuewen Technology
Oct 16, 2020 · Artificial Intelligence

How Intelligent Traffic Distribution Boosts New Book Exposure in Reading Apps

This article describes the design and implementation of an intelligent traffic distribution system for a reading platform, detailing its background, overall architecture, sub-modules such as the small‑traffic experiment platform, near‑line computation, retrieval strategies, pacing algorithms, and how it balances user personalization with content ecosystem growth.

AIBig DataReal-time Streaming
0 likes · 8 min read
How Intelligent Traffic Distribution Boosts New Book Exposure in Reading Apps
58UXD
58UXD
Sep 15, 2020 · Artificial Intelligence

How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights

This article explores the fundamentals and evaluation of recommendation systems, detailing their definition, key performance dimensions such as accuracy, diversity, novelty, serendipity, trust, and real‑time utility, and presents a practical case study from 58.com with reflections on methodology and future improvements.

Artificial IntelligenceEvaluation MetricsRecommendation Systems
0 likes · 12 min read
How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights
JD.com Experience Design Center
JD.com Experience Design Center
Aug 10, 2020 · Frontend Development

How JD’s ‘My Exclusive’ 618 Page Boosted Conversions with Front‑End Innovations

The article details JD.com’s “My Exclusive” 618 page redesign, describing how user‑centric browsing flows, exclusive visual atmospheres, and front‑end techniques like SVG gradients, IntersectionObserver animations, and dynamic gift‑pack effects boosted conversion rates and service growth during the promotion.

animationconversion optimizatione-commerce
0 likes · 12 min read
How JD’s ‘My Exclusive’ 618 Page Boosted Conversions with Front‑End Innovations
Meiyou UED
Meiyou UED
Jul 29, 2020 · Mobile Development

Designing an Effective Mobile Feed Flow: Principles, Market Insights & Usability

This article explores the concept and evolution of feed streams, analyzes the domestic market landscape across news and social apps, outlines usability design principles such as signal‑to‑noise ratio and Gestalt proximity, and proposes a redesign strategy to improve consistency, visual hierarchy, and user experience.

Usabilitycontent recommendationfeed design
0 likes · 16 min read
Designing an Effective Mobile Feed Flow: Principles, Market Insights & Usability
iQIYI Technical Product Team
iQIYI Technical Product Team
Jun 19, 2020 · Artificial Intelligence

From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey

iQIYI’s recommendation team transformed its offline, slow‑query system into a real‑time engine by sharding databases, adding caching, and adopting Kafka, Spark‑Streaming and Flink, cutting peak timeout from 4% to under 0.3%, delivering second‑level personalized, diverse, high‑quality video suggestions while keeping engineers close to the front‑line.

Recommendation SystemsiQIYImachine learning
0 likes · 7 min read
From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey
DataFunTalk
DataFunTalk
Jun 3, 2020 · Artificial Intelligence

Semantic Retrieval and Product Ranking in JD E‑commerce Search

This article presents JD's e‑commerce search system, detailing the semantic vector retrieval and product ranking pipelines, the two‑tower deep learning architecture, attention‑based personalization, negative sampling strategies, training optimizations, and real‑world performance gains achieved in production.

Deep LearningRankingVector Retrieval
0 likes · 11 min read
Semantic Retrieval and Product Ranking in JD E‑commerce Search
DataFunTalk
DataFunTalk
Apr 23, 2020 · Artificial Intelligence

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

The article explains how Alibaba Entertainment leverages causal inference and uplift modeling to build unbiased user‑cf recommendation algorithms that model user states and upgrade personalized distribution, achieving significant click‑through and re‑activation gains for long‑video services like Youku.

Recommendation SystemsVideo platformcausal inference
0 likes · 13 min read
Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms
Youku Technology
Youku Technology
Apr 2, 2020 · Artificial Intelligence

In‑Depth Overview of Intelligent Marketing Uplift Modeling

The talk explains uplift modeling for intelligent marketing, showing how causal lift predictions—derived from randomized experiments using two‑model, one‑model, or tree‑based methods—identify truly responsive users, evaluate performance with AUUC/Qini, and were applied to Taopiaopiao’s coupon allocation via knapsack optimization, highlighting challenges and future directions.

A/B testingUplift Modelingcausal inference
0 likes · 16 min read
In‑Depth Overview of Intelligent Marketing Uplift Modeling
Xianyu Technology
Xianyu Technology
Mar 17, 2020 · Backend Development

Evolution of Xianyu Push: From Offline 1.0 to Real‑time Intelligent Hermes Platform

From the manual offline 1.0 system to the real‑time, AI‑driven Hermes platform, Xianyu Push evolved through 1.1’s richer feed layout, personalized timing, and IFTTT‑style triggers, ultimately delivering higher click‑through rates, broader scene coverage, and stronger daily active user activation.

Push NotificationReal-time SystemScalable Design
0 likes · 8 min read
Evolution of Xianyu Push: From Offline 1.0 to Real‑time Intelligent Hermes Platform
Qunar Tech Salon
Qunar Tech Salon
Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR PredictionDeep LearningRanking
0 likes · 12 min read
Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 28, 2020 · Mobile Development

How Personalized Startup Task Scheduling Cuts Android Launch Time by 0.8 s

This article describes how Alibaba's HandCat team analyzed the growing startup latency of their Android app, identified static initialization bottlenecks, and implemented a data‑driven, per‑user and per‑device task orchestration using AOP instrumentation and algorithmic scheduling, achieving up to 1.6 s faster launches on low‑end devices.

AOP instrumentationPerformanceStartup Optimization
0 likes · 15 min read
How Personalized Startup Task Scheduling Cuts Android Launch Time by 0.8 s
HomeTech
HomeTech
Jan 15, 2020 · Artificial Intelligence

Architecture and Components of an Intelligent Recommendation Platform

The article outlines a micro‑service based intelligent recommendation platform that supports over 40 scenarios, detailing its overall architecture, AB testing service, and the three core modules—index, recall, and filter—while also describing future plans for platform centralization and open development.

AB testingAIEngine Architecture
0 likes · 5 min read
Architecture and Components of an Intelligent Recommendation Platform
Yanxuan Tech Team
Yanxuan Tech Team
Dec 16, 2019 · Artificial Intelligence

AI Powering NetEase Yanxuan: Supply Chain Forecasting, Personalization & Chatbots

This article explores how NetEase Yanxuan applies artificial intelligence across its e‑commerce platform, detailing machine‑learning‑driven supply‑chain sales forecasting, real‑time personalization algorithms, and intelligent customer‑service chatbots built with TensorFlow and advanced deep‑learning techniques.

AIChatbotpersonalization
0 likes · 12 min read
AI Powering NetEase Yanxuan: Supply Chain Forecasting, Personalization & Chatbots
DataFunTalk
DataFunTalk
Dec 2, 2019 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques

This article explains how Tencent tackles content understanding for personalized feed recommendation by combining traditional classification, keyword, and entity methods with deep learning embeddings, introducing an interest graph composed of taxonomy, concept, entity, and event layers to capture full context and infer user consumption intent.

NLPRecommendation Systemscontent understanding
0 likes · 14 min read
Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques
58 Tech
58 Tech
Nov 29, 2019 · Big Data

Application of Big Data and Algorithms in the Real‑Estate Internet

The talk presented at the Shanghai Computer Society Annual Meeting details how big data and algorithms are leveraged in the real‑estate internet sector to enhance user personalization, improve agent matching, and assess video quality, illustrating practical implementations and performance gains across data collection, modeling, and recommendation pipelines.

AIBig Dataalgorithm
0 likes · 10 min read
Application of Big Data and Algorithms in the Real‑Estate Internet
21CTO
21CTO
Nov 27, 2019 · Big Data

How Xiaohongshu Scales Real‑Time Personalized Recommendations with Flink

The article summarizes Guo Yi’s 2019 Alibaba Cloud conference talk, outlining Xiaohongshu’s personalized recommendation architecture, detailing the data stack from ingestion to warehouse, and showcasing a Flink‑based real‑time multi‑dimensional user behavior aggregation use case, followed by a vision for the next year’s data architecture evolution.

Data ArchitectureFlinkReal-time Streaming
0 likes · 3 min read
How Xiaohongshu Scales Real‑Time Personalized Recommendations with Flink
DataFunTalk
DataFunTalk
Nov 15, 2019 · Artificial Intelligence

From Zero to One: Building 58.com Recruitment Personalized Recommendation System

This article details how 58.com constructed a large‑scale personalized recommendation platform for its recruitment business, covering business background, user intent modeling, knowledge‑graph and NER techniques, user profiling, multi‑stage recall strategies, ranking model pipelines, serving infrastructure, AB testing, and future research directions.

CTRCVRRanking
0 likes · 18 min read
From Zero to One: Building 58.com Recruitment Personalized Recommendation System
网易UEDC
网易UEDC
Nov 6, 2019 · Product Management

How to Boost Platform Content Exposure with Smart Node‑Based Distribution

This article explores how designing various content nodes—based on user behavior, preferences, identity, social relationships, time, location, and multi‑dimensional attributes—can dramatically improve the efficiency and personalization of content distribution across digital platforms.

Content Distributionnode designpersonalization
0 likes · 10 min read
How to Boost Platform Content Exposure with Smart Node‑Based Distribution
DataFunTalk
DataFunTalk
Oct 25, 2019 · Artificial Intelligence

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

This article reviews recent progress and open research problems in human‑machine dialogue, covering both open‑domain chat and task‑oriented systems, with focus on reply quality, decoding, retrieval‑augmented generation, controllable and personalized responses, multi‑turn modeling, reinforcement‑learning strategies, low‑resource NLU, and data augmentation techniques.

Dialogue SystemsResponse Generationnatural language processing
0 likes · 16 min read
Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems
Youzan Coder
Youzan Coder
Oct 25, 2019 · Artificial Intelligence

Personalized Recommendation System Architecture and Techniques at Youzan

Youzan’s personalized recommendation platform combines a four‑layer architecture—data, storage, service, and application—with multi‑dimensional real‑time, offline, and cold‑start recall algorithms, Wide&Deep ranking, HBase/Druid storage, and configurable scene strategies to boost user conversion, traffic monetization, and future scalability.

HBaseWide&Deepcold-start
0 likes · 16 min read
Personalized Recommendation System Architecture and Techniques at Youzan
DataFunTalk
DataFunTalk
Oct 16, 2019 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

This article details Meitu's large‑scale personalized recommendation pipeline, describing the business scenario, challenges of massive data, latency and long‑tail distribution, and the application of deep learning techniques such as Item2vec, YouTubeNet, dual‑tower DNN, NFM, NFwFM and multi‑task learning to improve click‑through rate, conversion and user engagement.

Deep LearningLarge ScaleMulti-Task Learning
0 likes · 20 min read
Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking
DataFunTalk
DataFunTalk
Aug 14, 2019 · Artificial Intelligence

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

The article explores how the rapid growth of the internet has created information overload, discusses the challenges of recommendation systems such as sparsity and timeliness, outlines a four‑step personalized content pipeline, and highlights the interdisciplinary nature of building effective AI‑driven recommendation solutions.

AIBig DataData Engineering
0 likes · 16 min read
Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 7, 2019 · Artificial Intelligence

How KOBE Transforms Personalized Recommendation Reason Generation with Transformers

This article introduces KOBE, a knowledge‑based personalized text generation system that leverages Transformer architecture, attribute fusion, and external knowledge graphs to produce fluent, domain‑aware recommendation reasons for e‑commerce products, with a case study on the Spring Festival cloud theme.

Text GenerationTransformerknowledge graph
0 likes · 13 min read
How KOBE Transforms Personalized Recommendation Reason Generation with Transformers
Suning Technology
Suning Technology
Aug 2, 2019 · Big Data

How SuNing Uses Big Data to Revolutionize Retail Supply Chains

At the 15th China (Nanjing) International Software Expo, SuNing's VP shared how the company applies big‑data analytics, the C2M model, and flexible manufacturing to personalize retail experiences, bridge online‑offline gaps, and drive data‑driven product development and supply‑chain efficiency.

Big DataC2MData-Driven
0 likes · 9 min read
How SuNing Uses Big Data to Revolutionize Retail Supply Chains