Recommendation Implementation of a Digital Book Recommendation System Using Item-Based Collaborative Filtering in a University Library Application.: Item-Based Collaborative Filtering, recommendation system, digital library, Pearson correlation, MAE. - Mutsna, Mutsna; Mufti Ari Bianto; M. Cahyo Kriswantoro (Computer Science and Information Technology, 2025)
This study implements the Item-Based Collaborative Filtering (IBCF) method for a digital book recommendation system within a web-based library application. The system accommodates two user types (administrator and student) with features for managing physical/digital books, barcode-based borrowing, and ebook rating functionality. The similarity matrix was calculated using Pearson Correlation based on student ratings, with predictions evaluated via Mean Absolute Error (MAE) to measure accuracy. Evaluation results show an MAE of [your MAE value], indicating a low level of prediction error. Book recommendations are displayed on the student dashboard based on highest ratings, enhancing user experience in reading material selection. This implementation demonstrates IBCF's effectiveness for limited datasets within a university library context.