Diffusion-based knowledge aware recommendation systems

• Main Author: Qi, Yihan
• Other Authors: Andy Khong W H
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Engineering; Recommendation systems; Diffusion model; Knowledge graph
• Online Access: https://hdl.handle.net/10356/182792
• Institution: Nanyang Technological University

Knowledge-based recommendation systems are now essential for providing users with tailored content in the age of information overload. This dissertation in- vestigates DiffKG, a sophisticated diffusion-based knowledge graph model that uses structured information and diffusion mechanisms to improve the efficacy of recommendation systems. The suggested method improves recommendation quality by combining diffusion models with knowledge graphs to identify and take advantage of semantic links between entities. The paper shows that DiffKG is better than conventional techniques at producing accurate and pertinent sug- gestions through extensive experiments on real-world datasets. The study also examines the theoretical underpinnings and real-world applications of DiffKG across a range of fields, emphasizing its promise for scalable and explicable recommendation systems.