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Nov 9, 2025 · Backend Development

Designing a Scalable Real‑Time Video Ranking System for Billions of Views

This article walks through the complete system‑design process for a Douyin‑style video leaderboard, covering requirement analysis, functional and non‑functional specs, scale estimation, single‑machine baseline, fault‑tolerance, sharding, time‑window handling with a double‑pointer method, and caching strategies to achieve low latency, high throughput, and precise results at massive scale.

High ThroughputSliding WindowSystem Design

0 likes · 26 min read

Designing a Scalable Real‑Time Video Ranking System for Billions of Views

Su San Talks Tech

Oct 9, 2025 · Backend Development

Designing a Scalable Real‑Time Video Ranking System for Billions of Views

This article walks through the end‑to‑end system design of a TikTok‑style video leaderboard, covering functional and non‑functional requirements, traffic estimation, single‑node prototypes, fault‑tolerant replication, sharding, elastic partitioning, sliding‑window counters, and caching strategies to achieve low‑latency, high‑throughput ranking at massive scale.

Sliding WindowSystem Designcaching

0 likes · 21 min read

Kuaishou Tech

Oct 21, 2022 · Artificial Intelligence

Real-time Short Video Recommendation on Mobile Devices: System Design, Model Architecture, and Experimental Evaluation

The paper presents a lightweight on‑device re‑ranking system for short‑video recommendation that leverages real‑time user feedback and context‑aware generative ranking, detailing its architecture, feature engineering, beam‑search optimization, and both offline and online experimental results showing significant performance gains.

Beam SearchContext-Awarefeature engineering

0 likes · 12 min read

Real-time Short Video Recommendation on Mobile Devices: System Design, Model Architecture, and Experimental Evaluation

DaTaobao Tech

Mar 22, 2022 · Artificial Intelligence

Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel

The article details Alibaba’s end‑to‑end online‑learning pipeline for real‑time ranking in the Taobao home‑decor channel, covering UT log parsing, full‑feature extraction, ODL sample creation, xDeepCTR model training, and deployment, which yielded up to 7.8% CTR improvement and demonstrates the value of rapid model adaptation.

AlibabaModel TrainingOnline Learning

0 likes · 15 min read

Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel

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

Architecture Digest

Aug 12, 2021 · Backend Development

Implementing Real-Time Leaderboards with Redis in PHP

This article explains how to design and implement a real-time ranking system for a mobile tank game using Redis sorted sets, covering leaderboard categories, composite scoring formulas, dynamic updates, data retrieval with pipelines, and provides a complete PHP class example.

Composite ScorePHPSorted Set

0 likes · 10 min read

Implementing Real-Time Leaderboards with Redis in PHP

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.

Edge ComputingMeta LearningMobile AI

0 likes · 19 min read

EdgeRec: Edge Computing in Recommendation Systems

DataFunTalk

Oct 1, 2020 · Artificial Intelligence

Building and Applying a Vector System for Search and Recommendation at NetEase Yanxuan

This article describes how NetEase Yanxuan has designed, trained, and deployed a unified vector representation system to power various e‑commerce search and recommendation scenarios, covering model choices, incremental learning strategies, large‑scale similarity computation, and practical lessons from real‑world deployments.

e‑commercelarge-scale similaritymachine learning

0 likes · 18 min read