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.
DOI:
https://doi.org/10.37859/coscitech.v6i2.10011
Abstract
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.
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References
R. Ardiansyah, B. D. Saputra, dan M. A. Bianto, “Sistem Rekomendasi Buku Perpustakaan Sekolah Menggunakan Metode
Content-Based Filtering,” J. Computer Science and Information Technology (CoSciTech), vol. 4, no. 2, pp. 510–517, Aug.
F. Fitriyeh, N. Haqqi, L. M. Choiriyah, dan N. Ifada, “Dampak pembobotan pada metode hybrid user-based dan item-based
untuk sistem rekomendasi film,” J. Computer Science and Information Technology (CoSciTech), vol. 5, no. 3, pp. 516–525,
Dec. 2024, doi:10.37859/coscitech.v5i3.7637.
D. Roy dan F. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol.
, art. no. 59, 2022, doi:10.1186/s40537-022-00592-5.
Y. Li, K. Liu, R. Satapathy, S. Wang, dan E. Cambria, “Recent Developments in Recommender Systems: A Survey,” arXiv
preprint arXiv:2306.12680, Jun. 2023.
F. Fkih, “Similarity measures and evaluation metrics in recommender systems,” Journal of King Saud University —
Computer and Information Sciences, vol. 34, pp. 7645–7669, 2022, doi:10.1016/j.jksuci.2021.09.014.
Y. Wu, “Improved item-based collaborative filtering similarity measures,” International Journal of Computational
Intelligence Systems, vol. 15, 2022, doi:10.1007/s44196-022-00068-7.
(Hybrid neural + attention) Hybrid neural recommendation model with review-level attention, Computational Intelligence
and Neuroscience, 2022, Art. ID 2347641.
J. Bobadilla, F. Ortega, A. Hernando, dan A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Systems, vol.
, pp. 109–132, 2013.
A. (penulis arXiv), “Design, dataset characterization and KPI for book-recommender experiments,” arXiv preprint
arXiv:2303.11746, Mar. 2023.
N. F. D. Putri, N. Ifada, dan M. K. Sophan, “Normalization based Multi-Criteria Collaborative Filtering Approach for
Recommendation System,” Rekayasa, vol. 13, no. 3, pp. 234–239, 2020.
