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ranking models

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Architect
Architect
Jun 10, 2023 · Artificial Intelligence

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Twitter’s recently open‑sourced recommendation system is dissected, covering its overall architecture, graph‑based data and feature engineering, recall pipelines (in‑network and out‑of‑network), coarse and fine ranking models, mixing and re‑ranking stages, as well as the supporting infrastructure and code examples.

Machine LearningTwittergraph embedding
0 likes · 16 min read
An Overview of Twitter’s Open‑Source Recommendation System Architecture
DataFunSummit
DataFunSummit
Mar 24, 2022 · Artificial Intelligence

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

This article provides a comprehensive introduction to Learning to Rank (LTR), describing its four major categories—point‑wise, pair‑wise, list‑wise, and generative models—along with typical algorithms such as Wide & Deep, ESMM, RankNet, LambdaRank, LambdaMART, DLCM, and miRNN, and discusses their architectures, loss functions, and practical considerations in advertising and recommendation systems.

Generative ModelsMachine LearningPairwise
0 likes · 22 min read
An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches
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
58 Tech
58 Tech
Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB testingAIdeep learning
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution
Qunar Tech Salon
Qunar Tech Salon
Feb 27, 2020 · Artificial Intelligence

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

This article describes iQIYI’s dual‑DNN ranking architecture that combines a high‑capacity teacher network with a lightweight student network via online knowledge distillation, addressing the trade‑off between model effectiveness and inference efficiency in large‑scale recommendation systems.

CTR predictiondual DNNfeature interaction
0 likes · 12 min read
iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation
DataFunTalk
DataFunTalk
Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Recommendation systemsdeep learningknowledge distillation
0 likes · 13 min read
Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI