Knowledge Graphs for Explaination of Black-Box Recommender System

Abstract

Machine learning models, particularly black-box, make powerful decisions and recommendations. However, these models lack transparency and hence cannot be explained directly. The respective decisions need explanation with the help of techniques to gain users' trust and ensure the correct interpretation of a particular recommendation. Nowadays, Knowledge graphs (K-graph) has been recognized as a powerful tool to generate explanations for the predictions or decisions of black-box models. The explainability of the machine learning models enhances transparency between the user and the model. Further, this could result in better decision support systems, improvised recommender systems, and optimal predictive models. Unfortunately, while these black box devices have no detail on the reasons behind their forecasts, they lack clarity. White box structures, on the other hand, will quickly produce interpretations due to their existence. The chapter presents an exhaustive review and step-by-step description for using knowledge graphs in generating explanations for black-box recommender systems, which further helps in generating more persuasive and personalized explanations for the recommended items. We also implement a case study on the MovieLens dataset and WikiData using K-graph to generate accurate explanations.