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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主要な著者:	Dohyoung Rim, Sirojiddin Nuriev, Younggi Hong
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2025-01-01
シリーズ:	IEEE Access
主題:	Latent traits collaborative filtering matrix factorization dual deep neural networks cyclic training recommendation systems
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10829575/
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