Improving News Recommendation Accuracy Through Multimodal Variational Autoencoder and Adversarial Training

Improving News Recommendation Accuracy Through Multimodal Variational Autoencoder and Adversarial Training

Traditional recommender systems, such as collaborative filtering and content filtering, have inherent limitations, including cold start issues and challenges in filtering information. Additionally, the sparsity and noise in news data significantly affect recommendation accuracy. To address these iss...

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Bibliographic Details
Main Authors: Pei Tang, Shuo Zhu, Bilal Alatas
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Variational autoencoder
generative adversarial networks
multimodal recommender systems
multi-head attention mechanismmeta-learning
deep learning
convolutional neural networks
Online Access:https://ieeexplore.ieee.org/document/10994425/
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