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dc.contributor.authorJesús, Bobadilla-
dc.contributor.authorFernando, Ortega-
dc.contributor.authorAbraham, Gutiérrez-
dc.date.accessioned2023-03-31T03:41:09Z-
dc.date.available2023-03-31T03:41:09Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-022-08088-2-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7372-
dc.descriptionCC BYvi
dc.description.abstractDeep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectcollaborative filteringvi
dc.subjectmatrix factorizationvi
dc.titleDeep variational models for collaborative filtering-based recommender systemsvi
dc.typeBookvi
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