E-commerce platforms heavily rely on automatic personalized recommender systems, e.g., collaborative filtering models, to improve customer experience. Some hybrid models have been proposed recently to address the deficiency of existing models. However, their performances drop significantly when the dataset is sparse. Most of the recent works failed to fully address this shortcoming. At most, some of them only tried to alleviate the problem by considering either user side or item side content information. In this article, we propose a novel recommender model called Hybrid Variational Autoencoder (HVAE) to improve the performance on sparse datasets. Different from the existing approaches, we encode both user and item information into a latent space for semantic relevance measurement. In parallel, we utilize collaborative filtering to find the implicit factors of users and items, and combine their outputs to deliver a hybrid solution. In addition, we compare the performance of Gaussian distribution and multinomial distribution in learning the representations of the textual data. Our experiment results show that HVAE is able to significantly outperform state-of-the-art models with robust performance.

中文翻译：

---

推荐系统的混合变分自动编码器

电子商务平台严重依赖自动个性化推荐系统（例如协同过滤模型）来改善客户体验。最近提出了一些混合模型来解决现有模型的不足。但是，当数据集稀疏时，它们的性能会显着下降。最近的大部分作品都未能完全解决这个缺点。最多，他们中的一些人只是试图通过考虑用户侧或项目侧内容信息来缓解问题。在本文中，我们提出了一种新的推荐模型，称为混合变分自动编码器 (HVAE)，以提高稀疏数据集的性能。与现有方法不同，我们将用户和项目信息编码到潜在空间中以进行语义相关性测量。在平行下，我们利用协同过滤来找到用户和项目的隐含因素，并结合他们的输出来提供一个混合解决方案。此外，我们比较了高斯分布和多项分布在学习文本数据表示方面的性能。我们的实验结果表明，HVAE 能够以强大的性能显着优于最先进的模型。