Although recommender systems enjoy widespread adoption in numerous different production settings, standard methods draw only on previous purchases or ratings, and optionally simple customer or product features. In many domains, however, the purchase or rating history is very sparse. Standard approaches suffer from such data sparsity and neglect to account for important additional dependencies that can be taken into consideration. This motivates us to design a recommendation model with the ability to exploit hierarchical relationships such as product series, manufacturers, or even suppliers. To this end, we propose our Ontology Guided Multi-Relational Tensor Factorization model, which models such connections via a multilevel tree structure. To solve the challenging optimization problem, we develop an efficient iterative algorithm relying on Moreau-Yosida regularization and analyzed the complexity. On real-world data crawled from automobile-related websites, we find that the proposed model outperforms state-of-the-art methods.

尽管推荐系统在许多不同的生产环境中得到了广泛采用，但是标准方法仅基于先前的购买或评级以及可选的简单客户或产品功能。但是，在许多域中，购买或评级历史都很稀疏。标准方法遭受这样的数据稀疏性的影响，并且忽略了可以考虑的重要附加依赖关系。这促使我们设计一种推荐模型，该模型具有开发层次关系的能力，例如产品系列，制造商，甚至供应商。为此，我们提出了本体论指导的多关系张量因子分解模型，该模型通过多级树结构对此类连接进行建模。为了解决具有挑战性的优化问题，我们基于Moreau-Yosida正则化开发了一种有效的迭代算法，并分析了其复杂性。在从与汽车相关的网站上爬取的真实数据上，我们发现所提出的模型优于最新方法。