Interpretable recommendation based on graph neural networks

• Main Author: Tan, Samantha Shu Hua
• Other Authors: Luo Siqiang
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Graph neural network; Large language model; Explainable artificial intelligence; Explainable user interface
• Online Access: https://hdl.handle.net/10356/181192
• Institution: Nanyang Technological University

This project focuses on enhancing the explainability of Graph Neural Network (GNN)-based recommender systems by integrating Large Language Models (LLMs) and Explainable User Interface (XUI) design principles to deliver user-friendly, interpretable recommendations. Through the development of a LightGCN model paired with an three different LLMs, namely OpenAI’s GPT 4o-mini, Meta’s Llama 2.5 and Gemini 1.5, the system translates complex model predictions into natural language explanations, improving accessibility for novice users. By bridging technical GNN outputs with a user-centred design, this research addresses critical gaps in transparency and usability in XAI, demonstrating a practical approach for deploying interpretable, AI-driven recommendations in real-world applications.