BestHub
Sign in
Tagged articles
216 articles
Page 3 of 3
Meituan Technology Team
Meituan Technology Team
Jun 16, 2017 · Artificial Intelligence

Evolution of Meituan Travel Search Recall Strategies

Meituan‑Dianping’s travel search team tackles cross‑region queries and noisy data by iteratively refining a four‑step, case‑driven pipeline that classifies intent, segments queries, ranks results with distance and term‑importance models, and employs multi‑stage, parallel recall to steadily boost purchase rate, CTR, and user satisfaction.

SearchTravelintent classification
0 likes · 20 min read
Evolution of Meituan Travel Search Recall Strategies
21CTO
21CTO
Jun 7, 2017 · Artificial Intelligence

How Pinterest Scaled Its Recommendation Engine: From Simple Graphs to Real‑Time AI Ranking

This article chronicles Pinterest's recommendation system evolution, detailing how the platform progressed from basic pin‑board co‑occurrence graphs to sophisticated machine‑learning‑driven candidate generation and real‑time personalized ranking, boosting user engagement and enabling advanced visual search capabilities.

AIPinterestcandidate generation
0 likes · 15 min read
How Pinterest Scaled Its Recommendation Engine: From Simple Graphs to Real‑Time AI Ranking
Architecture Digest
Architecture Digest
Apr 2, 2017 · Artificial Intelligence

Mogujie's Search System Architecture and Online Request Flow

This article introduces Mogujie's end‑to‑end search system architecture, detailing its online and offline components such as Topn, ABTest, QR, fine‑ranking, search engine, UPS, and feature platforms, and then walks through a real‑world online request example to illustrate how queries are processed, rewritten, personalized, and finally ranked.

MogujieQuery RewriteSearch Architecture
0 likes · 11 min read
Mogujie's Search System Architecture and Online Request Flow
Meituan Technology Team
Meituan Technology Team
Mar 24, 2017 · Artificial Intelligence

Tourism Recommendation System: Strategy Iterations, Architecture, and Future Challenges

The article outlines Meituan‑Dianping’s tourism recommendation system, detailing its evolution from simple hot‑sale recall to sophisticated decay‑based, GPS‑aware, collaborative filtering and XGBoost reranking pipelines, the four‑layer architecture supporting dozens of travel scenarios, and future plans to broaden recall, adopt deep models, and expand multimodal travel recommendations.

Big DataTourismarchitecture
0 likes · 26 min read
Tourism Recommendation System: Strategy Iterations, Architecture, and Future Challenges
Ctrip Technology
Ctrip Technology
Jan 5, 2017 · Artificial Intelligence

Design and Implementation of Ele.me's Fast‑Iterating Online Recommendation System

This article details how Ele.me built a rapidly iterating recommendation system, covering model ranking architectures (single, linear, multi), online feature computation pipelines, feature management, and shuffling logic to balance algorithmic relevance with user perception, providing practical insights for large‑scale personalized services.

feature-engineeringmachine-learningonline system
0 likes · 13 min read
Design and Implementation of Ele.me's Fast‑Iterating Online Recommendation System
Ctrip Technology
Ctrip Technology
Sep 19, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature

This article describes Qunar's personalized demand prediction system for the "Guess You Like" card, detailing how user‑demand associations are mined via rule engines, collaborative filtering, LBS and manual rules, and how ranking models evolve from subjective Bayes to RankBoost and LambdaMart, with experimental evaluation and future LSTM plans.

AITravelmachine learning
0 likes · 10 min read
Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature
Qunar Tech Salon
Qunar Tech Salon
Aug 20, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card

This article describes how Qunar replaced a low‑click hot‑words card with a personalized “You May Like” recommendation card, detailing data collection, rule‑based and collaborative‑filtering association methods, learning‑to‑rank models (subjective Bayes, RankBoost, LambdaMart), training‑sample strategies, online experiments, evaluation metrics, and future plans including LSTM‑based sequence modeling.

Qunarcollaborative filteringmachine learning
0 likes · 14 min read
Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card
Architect
Architect
Apr 23, 2016 · Artificial Intelligence

Architecture and Techniques of an E‑commerce Search Engine

The article explains the overall architecture of an e‑commerce search engine, covering indexing, static scoring, retrieval, title and store deduplication, query analysis and rewriting, and related big‑data and AI techniques used to improve relevance and diversity of search results.

Query Rewritingdeduplicatione‑commerce
0 likes · 14 min read
Architecture and Techniques of an E‑commerce Search Engine
21CTO
21CTO
Mar 4, 2016 · Artificial Intelligence

How Facebook’s News Feed Works: Architecture, Culture, and Ranking Secrets

This article shares insights from former Facebook engineers on the company’s engineering culture, open workspace, code‑review practices, and the technical architecture behind the News Feed, including real‑time publishing, push/pull models, and machine‑learning‑driven ranking.

FacebookSystem Architecturenews feed
0 likes · 10 min read
How Facebook’s News Feed Works: Architecture, Culture, and Ranking Secrets
Qunar Tech Salon
Qunar Tech Salon
Jan 13, 2016 · Artificial Intelligence

Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements

This article presents a comprehensive overview of ranking learning techniques used in Mobile Taobao's recommendation system, covering problem definition, pointwise/pairwise/listwise approaches, feature engineering, online learning, industry applications, and future optimization strategies.

CTR predictionLambdaMARTlistwise
0 likes · 8 min read
Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements
21CTO
21CTO
Nov 20, 2015 · Artificial Intelligence

How Meituan Builds and Optimizes Its Recommendation System

This article explains Meituan's end‑to‑end recommendation system architecture, data processing pipeline, candidate generation strategies, model training and online ranking techniques, illustrating how data, algorithms, and real‑time signals are combined to improve relevance and conversion.

AIMeituandata engineering
0 likes · 19 min read
How Meituan Builds and Optimizes Its Recommendation System
Architect
Architect
Nov 15, 2015 · Big Data

An Introduction to Search Engine Architecture and Core Technologies

This article provides a comprehensive overview of search engine fundamentals—including inverted indexing, tokenization, ranking, high‑concurrency infrastructure, caching, crawling strategies, query understanding, keyword rewriting, personalization, and knowledge‑base construction—highlighting the technical challenges that make modern search engines like Google superior to simpler implementations.

crawlinginformation retrievalranking
0 likes · 14 min read
An Introduction to Search Engine Architecture and Core Technologies
21CTO
21CTO
Aug 21, 2015 · Artificial Intelligence

How Quora Leverages Machine Learning for Ranking, Personalization, and Moderation

Quora employs a variety of machine‑learning techniques—from ranking and personalized feed algorithms to duplicate‑question detection, user expertise inference, and content moderation—optimizing both user experience and content quality through offline testing, online A/B experiments, and models such as logistic regression, gradient‑boosted trees, and neural networks.

moderationpersonalizationquora
0 likes · 11 min read
How Quora Leverages Machine Learning for Ranking, Personalization, and Moderation
21CTO
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
Meituan Technology Team
Meituan Technology Team
Jan 31, 2015 · Artificial Intelligence

Meituan Recommendation System Architecture and Optimization Practices

Meituan’s recommendation platform comprises a data layer, a multi‑strategy candidate generation layer, a fusion‑and‑filtering layer, and a ranking layer that uses additive‑grove tree ensembles and online‑updated logistic regression, leveraging extensive user behavior logs, location, query, graph and real‑time signals to deliver personalized deals.

Meituanmachine learningpersonalization
0 likes · 14 min read
Meituan Recommendation System Architecture and Optimization Practices