基于披萨偏好的汉堡推荐：用专家产品解决数据稀疏问题
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
摘要：
在本文中，我们描述了一种方法来解决数据稀疏性，并在用户偏好知识有限的领域中创建推荐。我们将变分自编码器协同过滤从单域扩展到多域。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。这种直觉可以发挥到极致，在跨域设置中，源域中的用户历史足以在目标域中生成高质量的推荐。因此，我们创建了一个产品的专家（POE）的建议，联合模型跨多个领域的用户项目交互架构。该方法对一个或多个域的丢失数据有弹性，这是现实生活中经常发现的情况。我们提出了两个广泛使用的数据集上的结果-亚马逊和Yelp，支持主张，全面的用户偏好知识导致更好的建议。令人惊讶的是，我们发现，在某些情况下，一个POE推荐，不访问目标域的用户表示可以超过一个强大的VAE推荐基线训练的目标域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
