Tagged articles

cold start

114 articles · Page 2 of 2

Aug 29, 2018 · Artificial Intelligence

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

An in‑depth look at Alibaba’s billion‑scale graph embedding framework—GES and EGES—reveals how side‑information‑enhanced embeddings address user long‑tail coverage and cold‑start challenges, improving recommendation diversity and discovery across massive e‑commerce datasets and enabling real‑time personalized ranking.

cold starte-commercegraph embedding

0 likes · 7 min read

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

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.

Personalized Recommendationcold startcontent diversity

0 likes · 17 min read

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

360 Tech Engineering

May 11, 2018 · Mobile Development

Testing SDK Startup Time: Cold, Warm, and Lukewarm Launches on Android

This article explains how to measure and analyze Android SDK startup performance by defining cold, warm, and lukewarm launches, describing a testing methodology that uses specific device, network, and process conditions, and providing ADB commands and shell scripts to capture total launch time.

AndroidSDK TestingStartup Performance

0 likes · 7 min read

Testing SDK Startup Time: Cold, Warm, and Lukewarm Launches on Android

360 Quality & Efficiency

May 9, 2018 · Mobile Development

Testing Startup Time of an Advertising SDK on Android

This article explains the concepts of cold, warm, and lukewarm Android app launches, outlines a testing methodology for measuring SDK startup times using ADB commands, and discusses practical considerations, test scenarios, and common pitfalls encountered during performance testing.

ADBAndroidSDK Testing

0 likes · 7 min read

Testing Startup Time of an Advertising SDK on Android

21CTO

Sep 15, 2017 · Artificial Intelligence

Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices

This article explains the objectives of recommendation systems, outlines four recommendation approaches, dives into personalized recommendation architecture and core algorithms, and discusses practical challenges such as real‑time processing, cold‑start, diversity, content quality, and exploration‑exploitation trade‑offs.

cold startcollaborative filteringpersonalization

0 likes · 16 min read

Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices

Architecture Digest

Sep 15, 2017 · Artificial Intelligence

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

This article explains the objectives of recommendation systems, compares popular recommendation approaches, details the components and algorithms of personalized recommendation pipelines, and discusses practical challenges such as real‑time processing, freshness, cold‑start, diversity, content quality, and surprise handling.

Data Pipelinecold startevaluation

0 likes · 15 min read

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

Tencent TDS Service

Aug 10, 2017 · Mobile Development

How to Achieve Near-Instant Android App Launch: Cold Start Optimization Guide

This article explains why Android apps suffer noticeable delays on cold start, breaks down the launch lifecycle, and provides practical optimizations—including reducing work in lifecycle callbacks, avoiding unnecessary delays, customizing window backgrounds, and using profiling tools—to dramatically cut startup time.

Androidcold start

0 likes · 10 min read

How to Achieve Near-Instant Android App Launch: Cold Start Optimization Guide

21CTO

Aug 4, 2017 · Artificial Intelligence

AI Behind Hulu's Video Recommendations: From Collaborative Filtering to Neural Nets

In this talk, Hulu’s research director Zhou Hanning explains the key factors influencing recommendation system performance, describes optimization goals, explores collaborative filtering, matrix factorization, and neural‑network approaches—including metadata‑driven transfer learning and cold‑start solutions for live streaming—and shares practical AI implementations that improve user experience and engagement.

AIcold startmetadata

0 likes · 10 min read

AI Behind Hulu's Video Recommendations: From Collaborative Filtering to Neural Nets

Tencent TDS Service

Nov 24, 2016 · Mobile Development

Boost Android Cold Start: Lessons from Redex and Interdex Optimization

After Facebook open‑sourced Redex, we explored its many optimizations and pitfalls, focusing on Interdex to reorder classes in the main dex, uncovering how class pre‑verification and hot‑patch instrumentation affect cold‑start performance, and sharing practical insights and remaining challenges for Android apps.

AndroidDEXInterdex

0 likes · 15 min read

Boost Android Cold Start: Lessons from Redex and Interdex Optimization

Tencent Music Tech Team

Nov 4, 2016 · Artificial Intelligence

How QQ Music Recommendation System Understands Your Preferences

The QQ Music recommendation system tackles cold‑start by first mixing Chinese and English tracks, then builds a six‑dimensional user profile (content, social, scenario, crowd, time, blacklist) and tags songs with six attributes, using content‑based, collaborative, matrix‑factorization and neural‑network models plus implicit co‑listening links, while acknowledging that final wisdom still comes from human listeners.

cold startcollaborative filteringmusic recommendation

0 likes · 11 min read

How QQ Music Recommendation System Understands Your Preferences

21CTO

Apr 12, 2016 · Artificial Intelligence

Designing System and Personalized Recommendation Engines with Mahout and Spark

This article explains the architecture of both system-wide and personalized recommendation modules, compares three recommendation strategies, details the use of Apache Mahout for collaborative filtering with Java code examples, and discusses cold‑start solutions within a Spark‑Hadoop stack.

MahoutSparkcold start

0 likes · 15 min read

Designing System and Personalized Recommendation Engines with Mahout and Spark

21CTO

Mar 18, 2016 · Artificial Intelligence

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

This article outlines ten practical key points—including leveraging explicit and implicit feedback, hybridizing algorithms, handling temporal and geographic factors, exploiting social ties, solving cold‑start issues, optimizing presentation, defining clear metrics, ensuring real‑time updates, and scaling big‑data processing—to help engineers design effective intelligent recommendation systems.

cold startdata miningevaluation

0 likes · 18 min read

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

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.

Personalized Recommendationcold startdata sparsity

0 likes · 31 min read

Top 10 Open Challenges Shaping the Future of Personalized Recommendation Systems

21CTO

Aug 14, 2015 · Artificial Intelligence

How Meituan Supercharges Local Services with Advanced Recommendation and Ranking

This article details Meituan's recommendation ecosystem, covering its key products, system goals, architecture, data pipelines, algorithms, cold‑start strategies, and the extensive ranking work—including modeling, sampling, bias removal, feature engineering, interleaving, and online learning—to dramatically boost user conversion.

cold startfeature engineeringranking

0 likes · 15 min read

How Meituan Supercharges Local Services with Advanced Recommendation and Ranking