EnsVAE: Ensemble Variational Autoencoders for Recommendations

EnsVAE: Ensemble Variational Autoencoders for Recommendations

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the i...

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Bibliographic Details
Main Authors: Ahlem Drif, Houssem Eddine Zerrad, Hocine Cherifi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Hybrid recommender systems
neural recommender models
collaborative filtering
content-based filtering
variational autoencoders
Online Access:https://ieeexplore.ieee.org/document/9224132/

Internet

https://ieeexplore.ieee.org/document/9224132/

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