Perbandingan Cosine Similarity dan Mean Squared Difference dalam Rekomendasi Buku Fiksi berbasis Item - Rosydah, Lucyta Qutsyaning; Widiyaningtyas, Triyanna (Universitas Hamzanwadi, 2024)
The need for recommendations is increasingly crucial in the digital era, especially with the abundance of fiction book data from e-book platforms and digital libraries. This study aims to evaluate the effectiveness of item-based collaborative filtering using cosine similarity and Mean Squared Difference (MSD) metrics for book recommendations. The knowledge discovery in databases method was applied, encompassing data selection, pre-processing, transformation, data mining, and evaluation. The dataset includes 100,000 user ratings obtained from Kaggle's