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23 articles

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Machine Learning Algorithms & Natural Language Processing

May 7, 2026 · Artificial Intelligence

Low‑Prompt ‘Pang Goose AI’ Lets Anyone Generate Videos and Dashboards Without Learning Complex Prompts

The article argues that while modern LLMs like ChatGPT and Gemini are powerful, their usage barriers are rising, and introduces ‘Pang Goose AI’, a low‑prompt AI agent that, through a pre‑built SOP system, can produce a one‑minute e‑commerce video or an interactive data‑dashboard with a single sentence, outperforming generic models and eliminating the need for users to master prompt engineering.

AI agentsAI product reviewSOP

0 likes · 12 min read

Low‑Prompt ‘Pang Goose AI’ Lets Anyone Generate Videos and Dashboards Without Learning Complex Prompts

NetEase Cloud Music Tech Team

Apr 23, 2024 · Mobile Development

Cloud Music User Push Notification Optimization: Practices and Insights

Cloud Music revamped its push‑notification system by separating business and channel layers, integrating a unified delivery platform, tailoring messages to Android manufacturers, adding new push channels, refining frequency and copy controls, and using AI‑generated creatives, which together doubled click‑through rates and nearly doubled total click users within two months.

A/B testingAIGC Content GenerationAlgorithm Optimization

0 likes · 23 min read

Cloud Music User Push Notification Optimization: Practices and Insights

ByteDance Data Platform

Aug 16, 2023 · Operations

How LeKe Scaled 1,200 Gyms Using Data‑Driven Ops, Agile Testing & AI Recommendations

In an interview with LeKe CTO Chengshi, the company’s rapid growth is attributed to three data‑powered capabilities—fine‑grained user operation, agile A/B‑testing, and AI‑driven personalized recommendation—enabled by Volcano Engine’s data platform and the data‑flywheel concept.

A/B testingDigital Transformationdata-driven operations

0 likes · 13 min read

How LeKe Scaled 1,200 Gyms Using Data‑Driven Ops, Agile Testing & AI Recommendations

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

DaTaobao Tech

Apr 28, 2023 · Artificial Intelligence

Multi-Scenario Recommendation Model

The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.

AIDeep LearningRecommendation Systems

0 likes · 23 min read

DaTaobao Tech

Dec 28, 2022 · Artificial Intelligence

Adaptive Multi-Scenario Modeling for Taobao Personalized Recommendation

On January 9 at 7 p.m., Alibaba senior algorithm engineer Zhang Yuanliang will present a scenario‑adaptive, self‑supervised model for multi‑scenario personalized recommendation, discussing its background, technical details, experimental results, and real‑world deployment within Taobao’s recommendation system.

AIAlibabamulti‑scenario modeling

0 likes · 1 min read

Adaptive Multi-Scenario Modeling for Taobao Personalized Recommendation

Bilibili Tech

Nov 25, 2022 · Artificial Intelligence

Design and Evolution of a Scalable Danmaku Personalized Recommendation System

The paper describes how Bilibili transformed its danmaku service from a simple, limited‑recall pipeline into a ten‑fold larger, KV‑store‑backed recommendation architecture that unifies engineering and AI layers, uses dynamic sharding and Redis locks, and ultimately boosts recall pool size, exposure, and experiment speed while reducing downgrade rates.

AI integrationBackend Engineeringdanmaku

0 likes · 20 min read

Design and Evolution of a Scalable Danmaku Personalized Recommendation System

DataFunSummit

Sep 3, 2021 · Artificial Intelligence

Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its multi‑scene architecture, user‑session modeling, graph‑based recommendation algorithms, cold‑start strategies, cross‑domain user mapping, and a hierarchical travel‑play tag system that together enable large‑scale, real‑time, thousand‑person‑one‑face marketing.

Graph Neural NetworkTravelcold start

0 likes · 20 min read

Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

DataFunTalk

Aug 4, 2021 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform

This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.

Deep LearningEmbeddingcross-domain learning

0 likes · 16 min read

Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform

DataFunTalk

May 31, 2021 · Artificial Intelligence

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

This article presents the challenges of travel‑related product search, explains why traditional rule‑based sorting is insufficient, and describes how Alibaba Flypig’s team built a deep‑learning based personalized ranking system—including architecture, model variants, experimental results, and future optimization directions—to improve conversion rates for flight and ticket searches.

AIDeep LearningRanking Models

0 likes · 9 min read

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

DataFunTalk

Aug 3, 2020 · Artificial Intelligence

Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its architecture, scenario and functional abstractions, user‑modeling pipelines, full‑stack traffic control, cold‑start techniques, cross‑domain mapping, heterogeneous graph modeling, and a hierarchical travel‑play tag system to achieve thousand‑person‑one‑face recommendation across daily and promotional scenes.

Graph Neural NetworkTravelcold start

0 likes · 22 min read

Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

DataFunTalk

Jan 7, 2020 · Artificial Intelligence

Personalized Poster Production and Distribution System for Video Recommendation

This article describes how iQIYI’s technical product team designed and implemented an AI‑driven personalized poster generation and distribution pipeline that automatically creates, ranks, and serves customized video posters, improving click‑through rates across TV and mobile platforms.

AIVideo platformcontent personalization

0 likes · 11 min read

Personalized Poster Production and Distribution System for Video Recommendation

iQIYI Technical Product Team

Jan 3, 2020 · Industry Insights

How iQIYI Boosted Click‑Through Rates with AI‑Powered Personalized Poster Generation

This article examines iQIYI's end‑to‑end personalized poster production and distribution system, detailing AI‑driven image cropping, smart frame extraction, feature extraction, multi‑armed bandit ranking, and online experiments that together significantly increased poster click‑through rates on TV and mobile platforms.

AI poster generationVideo platformfeature extraction

0 likes · 12 min read

How iQIYI Boosted Click‑Through Rates with AI‑Powered Personalized Poster Generation

58 Tech

Nov 15, 2019 · Artificial Intelligence

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

This article presents a comprehensive case study of how 58.com built a personalized recommendation system for its large‑scale recruitment platform, covering business background, data challenges, user modeling, recall strategies, ranking pipelines, system architecture, experimental infrastructure, and future research directions.

AB testingKnowledge Graphfeature engineering

0 likes · 18 min read

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

iQIYI Technical Product Team

Oct 18, 2019 · Artificial Intelligence

iQIYI Effect Advertising: Algorithm Architecture, Click‑Conversion Estimation, and Smart Bidding

The talk details iQIYI’s effect advertising system, describing its feed and in‑frame architecture, the oCPX billing model, multi‑stage recall‑ranking pipelines, real‑time feature engineering, online FM and Wide&Deep models for sparse conversion prediction, and a smart‑bidding mechanism that balances cost, quality, and volume.

Online Learningadvertising algorithmfeature engineering

0 likes · 11 min read

iQIYI Effect Advertising: Algorithm Architecture, Click‑Conversion Estimation, and Smart Bidding

DataFunTalk

May 21, 2019 · Artificial Intelligence

Multimodal Video Analysis and Its Applications: Intelligent Asset Management, Automatic Cover Generation, Knowledge Graph, and Search

This article presents a comprehensive overview of Alibaba's large entertainment division research on multimodal video analysis, covering intelligent video asset management, automated cover creation with personalized distribution, video knowledge graph construction, multimodal search techniques, and future directions in AI-driven media processing.

AIKnowledge Graphcover generation

0 likes · 17 min read

Multimodal Video Analysis and Its Applications: Intelligent Asset Management, Automatic Cover Generation, Knowledge Graph, and Search

DataFunTalk

Apr 22, 2019 · Artificial Intelligence

Understanding Personalized Recommendation: From Machine Learning Basics to E‑commerce Applications

This article explains how personalized recommendation systems work in e‑commerce platforms like Taobao, covering the fundamentals of machine learning, feature engineering, model building, evaluation, and the business motivations behind delivering unique, user‑specific product suggestions.

AIe‑commercemachine learning

0 likes · 22 min read

Understanding Personalized Recommendation: From Machine Learning Basics to E‑commerce Applications

Meitu Technology

Jun 25, 2018 · Artificial Intelligence

Meitu's Personalized Recommendation System: Architecture, Features, and Optimization Strategies

Meitu’s personalized recommendation platform for the Meipai app combines offline feature engineering, near‑real‑time streaming, and online serving to recall, estimate, and rank billions of short videos using multi‑modal content features, user profiling, online learning, cold‑start bandit strategies, and multi‑objective diversity optimization, delivering timely, diverse feeds across live, homepage, and video‑detail scenarios.

Online Learningcold startcontent diversity

0 likes · 17 min read

Meitu's Personalized Recommendation System: Architecture, Features, and Optimization Strategies

Meituan Technology Team

Nov 2, 2017 · Big Data

Meituan Dianping Technology Seminar

The Meituan Dianping Technology Seminar presents five expert‑led sessions covering big‑data recommendation strategies for homestays, the construction of Meitu’s data platform, personalized information‑flow implementation, cross‑city video‑conference communication systems, and the latest trends in front‑end technology, each illustrated with practical case studies.

AIData PlatformTech Seminar

0 likes · 4 min read

21CTO

Jul 12, 2016 · Product Management

How Toutiao’s Data‑Driven Naming and Recommendation Engine Shaped Modern News Apps

This article examines Toutiao’s product evolution—from strategic naming decisions validated by A/B testing to its data‑driven recommendation architecture—highlighting how lean product methodology, algorithmic personalization, and continuous experimentation underpin its success in the mobile news landscape.

A/B testingData-drivenalgorithmic personalization

0 likes · 8 min read

How Toutiao’s Data‑Driven Naming and Recommendation Engine Shaped Modern News Apps

Meitu Technology

Mar 11, 2016 · Artificial Intelligence

Meipai Personalized Recommendation Technology Journey

As Meipai’s user base exploded, the platform shifted from manual curation to sophisticated personalized recommendation algorithms—leveraging machine‑learning and data‑mining techniques, iterating through multiple generations, overcoming scalability and relevance challenges, and delivering rapid solutions that inspire future recommendation system designs.

MeipaiRecommendation Algorithmalgorithm evolution

0 likes · 1 min read

Meipai Personalized Recommendation Technology Journey

Architects Research Society

Dec 20, 2015 · Artificial Intelligence

Understanding Personalized Recommendation: Meaning, Differences, Scenarios, and Implementation

This article explains the significance of personalized recommendation, distinguishes it from traditional push services, outlines typical application scenarios, and details a step‑by‑step approach—including user profiling, behavior sampling, algorithm modeling, machine learning, and content lifecycle management—to build effective recommender systems.

information overloadpersonalized recommendationrecommender systems

0 likes · 7 min read

Understanding Personalized Recommendation: Meaning, Differences, Scenarios, and Implementation

21CTO

Sep 7, 2015 · Artificial Intelligence

Top 10 Open Challenges Shaping the Future of Personalized Recommendation Systems

This article surveys the fundamental misconceptions about personalized recommendation, distinguishes it from market segmentation and collaborative filtering, and then systematically presents ten critical research challenges—including data sparsity, cold‑start, scalability, diversity‑accuracy trade‑offs, system robustness, user behavior modeling, evaluation metrics, UI/UX, cross‑dimensional data integration, and social recommendation—each illustrated with examples and recent literature.

Evaluation Metricscold startdata sparsity

0 likes · 31 min read

Top 10 Open Challenges Shaping the Future of Personalized Recommendation Systems