An attention mechanism and residual network based knowledge graph-enhanced recommender system

Abstract

Recommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth increases. Additionally, the abundance of unreliable paths in a KG has a detrimental impact on feature representation learning. In this article, we propose a KG-enhanced recommender system based on residual network and attention mechanism, which can capture high-order connectivity and long-range dependencies of the KG. Specifically, a resource allocation approach is employed to calculate the resource amount, which is subsequently utilized to evaluate the path reliability of the KG. After completing path extraction, we employ an attention mechanism to capture semantic correlations and structural information. To leverage the KG for enhancing recommender systems, we design a deep residual network with shortcut connections, effectively amalgamating advanced and abstract features using deep neural networks. The introduction of shortcut connections not only facilitates the fitting of residual mappings but also mitigates potential issues such as gradient explosion and convergence difficulties due to excessive network depth. Extensive experiments conducted on three standard datasets over baseline methods have demonstrated the superiority of our proposed recommender system.