In today's digital landscape, recommendation systems are vital for personalized content delivery across e-commerce, social media, and online education. This paper presents a Semantic Web-Based Small Sample Data Recommendation Algorithm Using Weighted Mutual Information, addressing significant challenges posed by data sparsity in conventional recommendation methods. Traditional algorithms often struggle with limited user behavior and item data, resulting in poor recommendations. Our approach utilizes a weighted mut…