Ontology Guided Sparse Tensor Factorization for joint recommendation with hierarchical relationships

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.