A Review of Explainable Recommender Systems Utilizing Knowledge Graphs and Reinforcement Learning
Neeraj Tiwary, Shahrul Azman Mohd Noah, Fariza Fauzi, Siok Yee Tan
Abstract
This review paper addresses the research question of the significance of explainability in AI and the role of integrating KG and RL to enhance Explainable Recommender Systems (XRS). It surveys articles published from January 2015 to March 2024 on XRS, focusing on knowledge graphs (KGs) and reinforcement learning (RL) for achieving explainability in recommender systems. Employing a systematic methodology, it introduces a custom Python-based web scraper to efficiently navigate and extract relevant academic research papers from IEEE, ScienceDirect (Elsevier), ACM, and Springer online databases. The study encompasses the PRISMA methodology to conduct a thorough analysis and identify pertinent research works. This systematic literature review aims to provide a unified view of the field by reviewing eight existing XRS literature reviews and 29 pertinent XRS studies involving KG and RL from the specified period. It categorizes and analyses relevant research papers based on their implementation methodologies and explores significant contributions, encompassing perspectives on model-agnostic and model-intrinsic explanations.