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NetEase Cloud Music Tech Team
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 GenerationCloud Music
0 likes · 23 min read
Cloud Music User Push Notification Optimization: Practices and Insights
ByteDance Data Platform
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
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
DaTaobao Tech
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.

AIData ModelingRecommendation systems
0 likes · 23 min read
Multi-Scenario Recommendation Model
DaTaobao Tech
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
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 integrationScalable storagebackend engineering
0 likes · 20 min read
Design and Evolution of a Scalable Danmaku Personalized Recommendation System
DataFunSummit
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.

Cold StartTravelgraph neural network
0 likes · 20 min read
Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
DataFunTalk
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.

cross-domain learningdeep learninge-commerce
0 likes · 16 min read
Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform
DataFunTalk
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 learningpersonalized recommendation
0 likes · 9 min read
Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models
DataFunTalk
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.

Cold StartTravelgraph neural network
0 likes · 22 min read
Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
DataFunTalk
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.

AIMulti-armed banditcontent personalization
0 likes · 11 min read
Personalized Poster Production and Distribution System for Video Recommendation
58 Tech
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 testingFeature EngineeringRanking
0 likes · 18 min read
From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform
iQIYI Technical Product Team
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.

Feature Engineeringadvertising algorithmdeep learning
0 likes · 11 min read
iQIYI Effect Advertising: Algorithm Architecture, Click‑Conversion Estimation, and Smart Bidding
DataFunTalk
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.

AIcover generationknowledge graph
0 likes · 17 min read
Multimodal Video Analysis and Its Applications: Intelligent Asset Management, Automatic Cover Generation, Knowledge Graph, and Search
Meitu Technology
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.

Cold StartFeature Engineeringcontent diversity
0 likes · 17 min read
Meitu's Personalized Recommendation System: Architecture, Features, and Optimization Strategies
Meitu Technology
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.

Algorithm EvolutionBig DataData Mining
0 likes · 1 min read
Meipai Personalized Recommendation Technology Journey
Architects Research Society
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 overloadmachine learningpersonalized recommendation
0 likes · 7 min read
Understanding Personalized Recommendation: Meaning, Differences, Scenarios, and Implementation