Tagged articles

9 articles

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Jul 9, 2025 · Artificial Intelligence

How LAST Enables Real‑Time Learning for Re‑Ranking in E‑Commerce Recommendations

This article presents LAST, a novel Learning-at-Serving-Time approach that enables real‑time online learning for re‑ranking in industrial recommendation pipelines, eliminating feedback latency, detailing its architecture, challenges, experimental validation, and practical advantages over traditional online learning methods.

LAST algorithmRecommendation Systemsonline serving

0 likes · 12 min read

How LAST Enables Real‑Time Learning for Re‑Ranking in E‑Commerce Recommendations

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

Jul 25, 2023 · Artificial Intelligence

Real‑Time Deep Learning Training with PAI‑ODL: Architecture, Pipeline, and Key Technologies

This article introduces PAI‑ODL, a real‑time deep‑learning training platform that supports online model updates for search, advertising, and recommendation scenarios, detailing its pipeline modules, system architecture, large‑scale sparse model techniques, incremental model export, embedding store design, and performance optimizations that together enable low‑latency, high‑throughput serving.

PAI ODLReal-time TrainingRuntime Optimization

0 likes · 19 min read

Real‑Time Deep Learning Training with PAI‑ODL: Architecture, Pipeline, and Key Technologies

IEG Growth Platform Technology Team

Feb 14, 2022 · Artificial Intelligence

Multimodal Evolution and Application in Tencent Game Advertising System

This article describes the end‑to‑end multimodal modeling pipeline—covering text, image, and video understanding, model evolution from shallow to deep networks, key‑frame extraction, fine‑tuning, and multimodal fusion—used in Tencent's game ad exchange platform, along with practical deployment challenges and solutions.

AdvertisingCNNMultimodal Learning

0 likes · 22 min read

Multimodal Evolution and Application in Tencent Game Advertising System

iQIYI Technical Product Team

Nov 12, 2021 · Artificial Intelligence

iQIYI Generic Ranking Framework for Video Recommendation

iQIYI’s generic ranking framework unifies feature production, replay, training, and ranking into modular, configurable phases that handle offline and real‑time data, support diverse models, provide automated monitoring, and have been deployed across all platforms, delivering over 20% higher watch time and doubling first‑play videos.

feature engineeringonline servingranking framework

0 likes · 15 min read

iQIYI Generic Ranking Framework for Video Recommendation

Meituan Technology Team

May 13, 2021 · Artificial Intelligence

Design and Practice of Turing OS: An Online Service Framework for Machine Learning and Deep Learning at Meituan

Meituan’s Turing OS unifies the end‑to‑end machine‑learning lifecycle—data preprocessing, feature generation, model training, deployment, online prediction and A/B testing—through a lightweight SDK, plugin‑based algorithms, DAG orchestration, sandbox validation and replay tools, cutting algorithm iteration from days to hours while handling billions of daily predictions.

Algorithm PlatformModel Deploymentonline serving

0 likes · 31 min read

Design and Practice of Turing OS: An Online Service Framework for Machine Learning and Deep Learning at Meituan

Xueersi Online School Tech Team

Jan 15, 2021 · Artificial Intelligence

Recommendation System Architecture and Engineering Overview

This article presents a comprehensive overview of a recommendation system, covering its business background, purpose, detailed engineering architecture—including data sources, computation, storage, online learning, service and access layers—and discusses key challenges, module design, and practical reflections.

AB testingTensorFlowdata engineering

0 likes · 14 min read

Recommendation System Architecture and Engineering Overview

Alibaba Cloud Developer

Sep 28, 2018 · Artificial Intelligence

How Alibaba’s AI·OS Powers 10 Years of Search & Recommendation at Scale

Alibaba’s AI·OS, a decade‑old big‑data deep‑learning online serving platform, underpins the group’s search and recommendation services, delivering sub‑10‑second updates, supporting massive models, and integrating components like TPP, RTP, HA3, DII, and iGraph to drive efficient algorithm iteration and cloud‑scale innovation.

AIonline serving

0 likes · 12 min read

How Alibaba’s AI·OS Powers 10 Years of Search & Recommendation at Scale

Architect

Nov 16, 2015 · Artificial Intelligence

Meituan O2O Search Ranking System: Online Architecture and Techniques

This article describes Meituan's online search ranking architecture for O2O services, covering data pipelines, feature loading, ranking service workflow, A/B testing, model choices, cold‑start handling, and position bias mitigation, all tailored for mobile‑centric personalized ranking.

A/B testingfeature engineeringonline serving

0 likes · 14 min read