Review of Explainable Graph-Based Recommender Systems

TL;DR

This paper reviews the latest approaches in explainable graph-based recommender systems, focusing on their methods, explanation types, datasets, and evaluation techniques to guide future research.

Contribution

It provides a comprehensive categorization and analysis of state-of-the-art explainable graph-based recommender systems, highlighting gaps and future directions.

Findings

Categorization based on learning, explaining, and explanation types

Analysis of datasets and evaluation methods used

Identification of future research challenges

Abstract

Explainability of recommender systems has become essential to ensure users' trust and satisfaction. Various types of explainable recommender systems have been proposed including explainable graph-based recommender systems. This review paper discusses state-of-the-art approaches of these systems and categorizes them based on three aspects: learning methods, explaining methods, and explanation types. It also explores the commonly used datasets, explainability evaluation methods, and future directions of this research area. Compared with the existing review papers, this paper focuses on explainability based on graphs and covers the topics required for developing novel explainable graph-based recommender systems.