Factorization Machines

DataFunTalk

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.

Factorization MachinesMachine LearningRecommendation systems

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.

Factorization MachinesGBDTMachine Learning

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 predictionFactorization MachinesMachine Learning

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 ProcessingFactorization Machines

0 likes · 13 min read

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

DataFunTalk

Jul 9, 2020 · Artificial Intelligence

Cross‑Domain Recommendation and Heterogeneous Mixed‑Feed Ranking Practices in 58 Community

This article presents a comprehensive overview of 58 Community's recommendation ecosystem, detailing its business background, cross‑domain recommendation concepts, three key challenges, practical solutions such as cross‑domain collaborative filtering with factorization machines, attribute‑mapping and multi‑view DSSM approaches, as well as the engineering of heterogeneous mixed‑feed ranking using scoring alignment, MMR and DPP diversity algorithms, and reports significant online performance gains.

Factorization MachinesRankingcross-domain recommendation

0 likes · 27 min read

Cross‑Domain Recommendation and Heterogeneous Mixed‑Feed Ranking Practices in 58 Community

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.

Factorization MachinesMachine Learningevaluation

0 likes · 8 min read

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

DataFunTalk

Jul 31, 2019 · Artificial Intelligence

Key Characteristics and Practical Improvements of Recommendation Technologies

This article discusses the fundamental traits of recommendation technologies, compares UserCF and ItemCF models, explains matrix factorization and FM, explores negative sampling, CTR/CVR modeling, ensemble methods, and practical considerations such as reinforcement learning and exploration strategies for improving recommendation performance in real-world systems.

CTR predictionFactorization MachinesMachine Learning

0 likes · 11 min read

Key Characteristics and Practical Improvements of Recommendation Technologies

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.

Factorization MachinesRankingRecommendation systems

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.

Deep neural networksFactorization MachinesGBDT

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 MachinesMachine Learning

0 likes · 12 min read

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