Tagged articles

9 articles

Page 1 of 1

Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep LearningRecommendation Systemsfactorization machines

0 likes · 12 min read

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

iQIYI Technical Product Team

Jul 23, 2021 · Artificial Intelligence

XGBoost Serving: An Open‑Source High‑Performance Inference System for GBDT and GBDT+FM Models

XGBoost Serving is an open‑source, high‑performance inference system built on TensorFlow Serving that adds dedicated servables for pure GBDT, GBDT+FM binary‑classification, and GBDT+FM multi‑classification models, providing automatic version lifecycle management, GRPC/HTTP APIs, and up to 50 % latency reduction, now available on GitHub after successful deployment in iQIYI’s recommendation platform.

GBDTServing ArchitectureXGBoost Serving

0 likes · 12 min read

XGBoost Serving: An Open‑Source High‑Performance Inference System for GBDT and GBDT+FM Models

DataFunSummit

Feb 2, 2021 · Artificial Intelligence

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

This article systematically reviews the evolution of click‑through‑rate (CTR) prediction models—from early distributed linear models like logistic regression, through automated feature engineering with GBDT+LR, various factorization‑machine variants, embedding‑MLP shallow modifications, dual‑tower combinations, and advanced explicit feature‑cross networks—highlighting each model’s structure, advantages, limitations, and comparative insights.

CTR predictionclick-through ratefactorization machines

0 likes · 28 min read

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

DataFunTalk

Nov 17, 2020 · Artificial Intelligence

Alink: A Flink‑Based Machine Learning Platform – Overview, Features, and Quick‑Start Guide

This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.

AlinkBatch ProcessingData Integration

0 likes · 13 min read

Alink: A Flink‑Based Machine Learning Platform – Overview, Features, and Quick‑Start Guide

DataFunTalk

Aug 30, 2019 · Artificial Intelligence

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

This article reviews the TransFM model, which combines the translation‑based sequential recommendation approach (TransRec) with factorization machines (FM), explains its formulation, optimization via sequential Bayesian personalized ranking, and demonstrates its superior performance on Amazon and Google Local datasets compared with several baselines.

evaluationfactorization machinesmachine learning

0 likes · 8 min read

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

Alibaba Cloud Developer

Sep 14, 2018 · Artificial Intelligence

How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM

This article details the evolution of Alibaba's short‑video feed ranking system, from a Wide&Deep CTR model to a hybrid Factorization‑Machine and Gradient‑Boosted‑Tree approach, describing feature engineering, model architecture, experimental results, lessons learned, and future directions toward duration‑based relevance.

factorization machinesgradient boostingmachine learning

0 likes · 11 min read

How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM

Tencent Cloud Developer

Mar 16, 2018 · Artificial Intelligence

Pairwise Ranking Factorization Machines (PRFM) for Feed Recommendation in Tencent Shield

The article presents Pairwise Ranking Factorization Machines (PRFM), a pairwise‑learning extension of Factorization Machines that replaces Tencent Shield’s pointwise binary‑classification pipeline, generates user‑item‑item triples, optimizes a cross‑entropy loss, and achieves about a 5% relative UV click‑through gain on the HandQ anime feed while outlining offline metrics, hyper‑parameter tuning, and future informed‑sampling enhancements.

Recommendation Systemsfactorization machinespairwise learning

0 likes · 10 min read

Pairwise Ranking Factorization Machines (PRFM) for Feed Recommendation in Tencent Shield

iQIYI Technical Product Team

Nov 10, 2017 · Artificial Intelligence

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

The iQIYI recommendation system combines a two‑stage pipeline of recall and ranking, evolving from logistic regression to a GBDT‑FM‑DNN ensemble, using online feature storage, extensive feature engineering, and configurable strategies to deliver personalized video suggestions while addressing feature drift and multi‑objective business goals.

GBDTRecommendation Systemsdeep neural networks

0 likes · 13 min read

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

Ctrip Technology

May 6, 2017 · Artificial Intelligence

Product Matching in E‑commerce: Rule‑based, Feature‑Engineering, and Pure Data‑driven Approaches Using Factorization Machines

This article examines e‑commerce product matching, comparing rule‑based methods, feature‑engineering models, and a pure data‑driven Factorization Machine approach, detailing their advantages, challenges, training techniques, and successive optimizations to improve matching accuracy and operational efficiency.

e‑commercefactorization machinesproduct matching

0 likes · 12 min read

Product Matching in E‑commerce: Rule‑based, Feature‑Engineering, and Pure Data‑driven Approaches Using Factorization Machines