  Deep learning provides accurate collaborative filtering models to improve
recommender system results. Deep matrix factorization and their related
collaborative neural networks are the state-of-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. Our proposed models apply the variational concept to
inject stochasticity in the latent space of the deep architecture, introducing
the variational technique in the neural collaborative filtering field. This
method does not depend on the particular model used to generate the latent
representation. In this way, this approach can be applied as a plugin to any
current and future specific models. The proposed models have been tested using
four representative open datasets, three different quality measures, and
state-of-art baselines. The results show the superiority of the proposed
approach in scenarios where the variational enrichment exceeds the injected
noise effect. Additionally, a framework is provided to enable the
reproducibility of the conducted experiments.
