In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about 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 some 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.

翻译：在本文中,我们描述一种处理数据宽度的方法,并在对用户偏好有有限了解的领域提出建议。我们扩大了从单一域到多域设置的变式自动编码协作过滤器。直觉是,源域中的用户-项目互动可以提高目标域的建议质量。直觉可以发展到极端,在跨域设置中,源域的用户历史足以产生目标域的高质量建议。因此,我们为多个域联合模拟用户-项目互动的建议创建了产品-专家(POE)架构。该方法能够适应一个或多个域的缺失数据,这是现实生活中经常发现的情况。我们展示了两个广泛使用的数据集-亚马逊和耶尔普的结果,这两个数据集支持关于整体用户偏好知识导致更好的建议的说法。令人惊讶的是,我们发现在某些情况下,没有访问目标域用户代表的POE推荐器可以超过在目标域培训的强有力的VAE推荐基线。