Recent research has shown the advantages of using autoencoders based on deep
neural networks for collaborative filtering. In particular, the recently
proposed Mult-VAE model, which used the multinomial likelihood variational
autoencoders, has shown excellent results for top-N recommendations. In this
work, we propose the Recommender VAE (RecVAE) model that originates from our
research on regularization techniques for variational autoencoders. RecVAE
introduces several novel ideas to improve Mult-VAE, including a novel composite
prior distribution for the latent codes, a new approach to setting the $\beta$
hyperparameter for the $\beta$-VAE framework, and a new approach to training
based on alternating updates. In experimental evaluation, we show that RecVAE
significantly outperforms previously proposed autoencoder-based models,
including Mult-VAE and RaCT, across classical collaborative filtering datasets,
and present a detailed ablation study to assess our new developments. Code and
models are available at this https URL

该论文提出了基于变分自编码器的推荐 VAE（RecVAE）模型，通过多种方法改进了 Mult-VAE 模型并优于以前提出的基于自编码器的模型，实验结果表明 RecVAE 显著优于先前提出的基于自编码器的模型。

RecVAE：一种新的变分自编码器，用于使用隐式反馈的 Top-N 推荐

RecVAE: a New Variational Autoencoder for Top-N Recommendations with  Implicit Feedback

We extend variational autoencoders (VAEs) to collaborative filtering for
implicit feedback. This non-linear probabilistic model enables us to go beyond
the limited modeling capacity of linear factor models which still largely
dominate collaborative filtering research.We introduce a generative model with
multinomial likelihood and use Bayesian inference for parameter estimation.
Despite widespread use in language modeling and economics, the multinomial
likelihood receives less attention in the recommender systems literature. We
introduce a different regularization parameter for the learning objective,
which proves to be crucial for achieving competitive performance. Remarkably,
there is an efficient way to tune the parameter using annealing. The resulting
model and learning algorithm has information-theoretic connections to maximum
entropy discrimination and the information bottleneck principle. Empirically,
we show that the proposed approach significantly outperforms several
state-of-the-art baselines, including two recently-proposed neural network
approaches, on several real-world datasets. We also provide extended
experiments comparing the multinomial likelihood with other commonly used
likelihood functions in the latent factor collaborative filtering literature
and show favorable results. Finally, we identify the pros and cons of employing
a principled Bayesian inference approach and characterize settings where it
provides the most significant improvements.

本研究提出一种基于 VAE 的非线性概率模型，用于解决协作过滤问题，相对于现有的线性因子模型，具有更强的建模能力，并使用贝叶斯推断进行参数估计来得到更好的泛化性能。本方法在现有多个数据集上均取得了显著优异的性能表现，并对使用多项式分布作为似然函数进行了比较实验，分析了使用贝叶斯方法的优缺点及适用场景。