Cyclic Training of Dual Deep Neural Networks for Discovering User and Item Latent Traits in Recommendation Systems

Recommendation systems face the complex challenge of modeling high-dimensional interactions between users and items to deliver personalized recommendations. This paper introduces Cyclic Dual Latent Discovery (CDLD), a novel method that employs dual deep neural networks (DNNs) in a cyclic training pr...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Dohyoung Rim, Sirojiddin Nuriev, Younggi Hong
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2025-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Latent traits collaborative filtering matrix factorization dual deep neural networks cyclic training recommendation systems
Online-Zugang:	https://ieeexplore.ieee.org/document/10829575/
Tags:	Tag hinzufügen Keine Tags, Fügen Sie das erste Tag hinzu!