Title: Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms Show Chinese title

Title: 增强变分自编码器用于协同过滤：灵活先验与门控机制

Abstract: Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix). Show Chinese abstract

Abstract: 基于神经网络的协同过滤模型最近开始引起关注。其中一个研究分支是基于使用深度生成模型来建模用户偏好，变分自编码器被证明能产生最先进的结果。 然而，当前变分自编码器在协同过滤（CF）方面存在一些潜在的问题特征。首先是变分自编码器用于学习用户偏好的潜在表示所采用的过于简单的先验。另一个问题是该模型无法通过每个网络中的多于一个隐藏层来学习更深的表示。 我们的目标是结合适当的技术来缓解变分自编码器协同过滤的上述问题，并进一步提高推荐性能。我们的工作首次将灵活的先验应用于协同过滤，并表明简单的先验（如原始变分自编码器中的先验）可能过于限制性，无法充分建模用户偏好，而设置更灵活的先验可以带来显著的收益。我们尝试了最初为图像生成提出的VampPrior，以检验灵活先验在协同过滤中的效果。我们还展示了VampPrior与门控机制相结合，在两个流行的基准数据集（MovieLens和Netflix）上以有意义的差距超过了包括协同过滤变分自编码器在内的最新技术成果。