# Deep Variational Models for Collaborative Filtering-based Recommender Systems

arXiv: https://arxiv.org/abs/2107.12677 · HTML: https://mlsanity.com/paper/2107.12677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [A deep matrix factorization method for learning attribute representations](https://mlsanity.com/paper/1509.03248/) (2015)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder](https://mlsanity.com/paper/2004.12811/) (2020)
- [DeepFair: Deep Learning for Improving Fairness in Recommender Systems](https://mlsanity.com/paper/2006.05255/) (2020)
- [Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective](https://mlsanity.com/paper/2003.02474/) (2020)
- [Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens](https://mlsanity.com/paper/2008.01192/) (2020)
- [Providing reliability in Recommender Systems through Bernoulli Matrix Factorization](https://mlsanity.com/paper/2006.03481/) (2020)
- [Deep Learning feature selection to unhide demographic recommender systems factors](https://mlsanity.com/paper/2006.12379/) (2020)

## Abstract (arXiv)

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.