E-commerce businesses are increasingly dependent on recommendation systems to introduce personalized services and products to targeted customers. Providing effective recommendations requires sufficient knowledge about user preferences and product (item) characteristics. Given the current abundance of available data across domains, achieving a thorough understanding of the relationship between users and items can bring in more collaborative filtering power and lead to a higher recommendation accuracy. However, how to effectively utilize different types of knowledge obtained across domains is still a challenging problem. In this paper, we propose to discover both explicit and implicit similarities from latent factors across domains based on matrix tri-factorization. In our research, common factors in a shared dimension (users or items) of two coupled matrices are discovered, while at the same time, domain-specific factors of the shared dimension are also preserved. We will show that such preservation of both common and domain-specific factors are significantly beneficial to cross-domain recommendations. Moreover, on the non-shared dimension, we propose to use the middle matrix of the tri-factorization to match the unique factors, and align the matched unique factors to transfer cross-domain implicit similarities and thus further improve the recommendation. This research is the first that proposes the transfer of knowledge across the non-shared (non-coupled) dimensions. Validated on real-world datasets, our approach outperforms existing algorithms by more than two times in terms of recommendation accuracy. These empirical results illustrate the potential of utilizing both explicit and implicit similarities for making across-domain recommendations.

中文翻译：

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揭示跨域推荐的隐藏隐式相似性

电子商务企业越来越依赖推荐系统向目标客户介绍个性化服务和产品。提供有效的推荐需要对用户偏好和产品（项目）特征有足够的了解。鉴于当前跨域的可用数据非常丰富，深入了解用户和项目之间的关系可以带来更多的协同过滤能力，并导致更高的推荐准确性。然而，如何有效地利用跨领域获得的不同类型的知识仍然是一个具有挑战性的问题。在本文中，我们建议从基于矩阵三分解的跨域潜在因素中发现显式和隐式相似性。在我们的研究中，发现两个耦合矩阵的共享维度（用户或项目）中的公共因素，同时还保留共享维度的特定领域因素。我们将表明，这种对共同因素和特定领域因素的保留对跨领域推荐非常有益。此外，在非共享维度上，我们建议使用三因子分解的中间矩阵来匹配唯一因子，并将匹配的唯一因子对齐以传递跨域隐式相似性，从而进一步提高推荐。这项研究是第一个提出跨非共享（非耦合）维度进行知识转移的研究。在真实世界的数据集上进行了验证，我们的方法在推荐准确性方面比现有算法高出两倍以上。