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.

中文翻译：

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基于协同过滤的推荐系统的深度变分模型

深度学习提供准确的协同过滤模型来改进推荐系统的结果。深度矩阵分解及其相关的协作神经网络是该领域的最新技术；然而，这两种模型都缺乏必要的随机性来创建变分自编码器所展示的稳健、连续和结构化的潜在空间。另一方面，由于推荐系统的高度稀疏性，通过变分自编码器进行的数据增强在协同过滤领域不能提供准确的结果。我们提出的模型应用变分概念在深层架构的潜在空间中注入随机性，将变分技术引入神经协同过滤领域。该方法不依赖于用于生成潜在表示的特定模型。通过这种方式，这种方法可以作为插件应用于任何当前和未来的特定模型。已使用四个具有代表性的开放数据集、三个不同的质量度量和最先进的基线对所提出的模型进行了测试。结果显示了所提出的方法在变分富集超过注入噪声效应的情况下的优越性。此外，还提供了一个框架，以实现所进行实验的可重复性。