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

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...

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Bibliographic Details
Main Authors: Dohyoung Rim, Sirojiddin Nuriev, Younggi Hong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Latent traits
collaborative filtering
matrix factorization
dual deep neural networks
cyclic training
recommendation systems
Online Access:https://ieeexplore.ieee.org/document/10829575/
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https://ieeexplore.ieee.org/document/10829575/

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