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...

全面介紹

Saved in:
書目詳細資料
主要作者: Tan, Samantha Shu Hua
其他作者: Luo Siqiang
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2024
主題:
Computer and Information Science
Graph neural network
Large language model
Explainable artificial intelligence
Explainable user interface
在線閱讀:https://hdl.handle.net/10356/181192
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結: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.

相似書籍