In this paper we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi domain setting. The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain. The intuition can be taken to its extreme, where, in a cross-domain setup, the user history in a source domain is enough to generate high quality recommendations in a target one. We thus create a Product-of-Experts (POE) architecture for recommendations that jointly models user-item interactions across multiple domains. The method is resilient to missing data for one or more of the domains, which is a situation often found in real life. We present results on two widely-used datasets - Amazon and Yelp, which support the claim that holistic user preference knowledge leads to better recommendations. Surprisingly, we find that in select cases, a POE recommender that does not access the target domain user representation can surpass a strong VAE recommender baseline trained on the target domain. We complete the analysis with a study of the reasons behind this outperformance and an in-depth look at the resulting embedding spaces.

中文翻译：

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根据比萨偏好设置推荐汉堡：使用专家产品解决数据稀疏性

在本文中，我们描述了一种解决数据稀疏性的方法，并在对用户偏好的了解有限的情况下在域中创建推荐。我们将变式自动编码器协作过滤从单域扩展到多域设置。直觉是源域中的用户项目交互可以增强目标域中的推荐质量。直觉可以发挥到极致，在跨域设置中，源域中的用户历史记录足以在目标域中生成高质量的建议。因此，我们为建议创建了专家产品（POE）架构，以共同建模跨多个域的用户项目交互。该方法可以抵抗一个或多个域的数据丢失，这是现实生活中经常遇到的一种情况。我们在两个广泛使用的数据集上提供了结果-Amazon和Yelp，它们支持这样的说法，即整体用户偏好知识可带来更好的建议。令人惊讶的是，我们发现在某些情况下，不访问目标域用户表示的POE推荐程序可以超过在目标域上训练的强大VAE推荐程序基准。我们通过研究这种性能背后的原因并深入研究由此产生的嵌入空间来完成分析。