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

Abstract

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 process to discover the latent traits of users and items based on their interactions. CDLD operates on the principle that interactions between users and items inherently possess information about their latent traits. By cyclically refining these traits through two interconnected DNNs, CDLD effectively captures the non-linear relationships between them. When evaluated with the MovieLens 100K dataset, CDLD demonstrated superior performance, achieving a root mean square error (RMSE) that was 2.86% lower than that of matrix factorization and 2.51% lower than that of neural collaborative filtering, thus showing strong generalization capabilities. Additionally, CDLD addresses the cold-start problem effectively by adjusting only the new entity’s latent traits during training, while maintaining fixed model weights, even with limited data samples. The proposed method not only enhances the accuracy of recommendations but also improves the scalability and robustness of the model. Through its cyclical training mechanism, CDLD enables the ongoing refinement of latent traits, paving the way for more adaptive and efficient recommendation systems with diverse applications.

Keywords

• Latent traits
• collaborative filtering
• matrix factorization
• dual deep neural networks
• cyclic training
• recommendation systems