An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information

• Published in: Knowledge-based systems 2022-03, Vol.239, p.107970, Article 107970
• Main Authors: Shimizu, Ryotaro, Matsutani, Megumi, Goto, Masayuki
• Format: Article
• Language: English
• Subjects: Accuracy; Computational efficiency; Computing costs; Computing time; Decision making; Explainable artificial intelligence; Explainable recommendation; Graph theory; Knowledge graph attention network; Knowledge graph embedding; Knowledge graph enabled recommendation; Knowledge representation; Machine learning; Model-intrinsic approach; Recommender systems; User satisfaction
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.107970

In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ages and genders, and bookmark information, among others) to expound the decision-making reasoning applied by recommendation models. In explainable recommendation, although learning side information containing numerous variables leads to rich interpretability, learning too many variables presents a challenge because decreases the amount of learning that a given computational resource can perform, and the accuracy of the recommendation model may be degraded. However, numerous and diverse variables are included in the side information stored by the actual companies operating massive real-world services. Hence, to realize practical applications of this valuable information, it is necessary to resolve problems such as computational cost. In this study, we propose a new framework for explainable recommendation based on an improved knowledge graph attention network model, which utilizes the side information of items and realizes high recommendation accuracy. The proposed framework enables direct interpretation by visualizing the reasons for the recommendations provided. Experimental results show that the proposed framework reduced computational time requirements by approximately 80%, while maintaining recommendation accuracy by enabling the model to learn the probabilistically given edges included in the graph structure. Moreover, the results show that the proposed framework exhibited richer interpretability than the conventional model. Finally, a multifaceted analysis suggests that the proposed framework is not only effective as an explainable recommendation model but also provides a powerful tool for planning various marketing strategies. •Reduced the computational cost of knowledge graph enabled recommendation.•Improved explainability while maintaining high recommendation accuracy.•Introduced how to apply knowledge graph embedding to marketing strategy planning.•Presented strengths of explainable recommendation, not just explainability.•Improved value and applicability o