Interpretable recommendation based on graph neural networks

Interpretable recommendation based on graph neural networks

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 LightG...

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Bibliographic Details
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
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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