A proposed User-Based Approach for eBooks Recommendation Using a Weighted Nearest Neighbor Technique


  • Abdullah Mohammed Saleh College of Computer Sciences and Mathematics, University of Mosul, Nineveh, Iraq
  • Alaa Yaseen Taqa College of Education for Pure Sciences, University of Mosul, Nineveh, Iraq




Book-Crossing, Collaborative filtering, eBooks recommender, KNN with Weight, Pearson correlation, User-Based


Large book data stores were beneficial for our support systems but posed significant challenges for useful information retrieval. This issue was resolved by collaboratively filtering data depending on user needs. This study suggested a user-based methodology for recommending eBooks. The selected dataset was pre-processed, and Cross-validation was used to build a user-user similarity matrix. Three nearest neighbor algorithms (KNN Basic, KNN with Means and KNN with ZScore) were  used, and weighted KNN was proposed for rating prediction. In this technique, the weight of each user was calculated based on its distance from the intended user. The evaluation process depends on the user-item matrix and user-user matrix for prediction. The proposed recommendation system was tested on the book-crossing dataset, and the results were evaluated using the root mean square error and the mean absolute value of error. The results show that the error rate of the proposed model is the lowest compared to the other methods used, specifically when using the Pearson-Baseline technique. Since the root mean square error is 1.647 and the mean absolute value of errors is 1.253. When using the cosine technique, the root mean square error is 1.742, and the mean absolute value of errors is 1.328.

GS Cited Analysis


Download data is not yet available.


Ahmed, E., & Letta, A. (2023). Book Recommendation Using Collaborative Filtering Algorithm. Applied Computational Intelligence and Soft Computing, 2023, 12. doi:10.1155/2023/1514801

Alita, D., Putra, A. D., & Darwis, D. (2021). Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems). doi:10.22146/ijccs.65586

Bhaskaran, S., Marappan, R., & Santhi, B. (2020). Design and comparative analysis of new personalized recommender algorithms with specific features for large scale datasets. Mathematics. doi:10.3390/math8071106

Feng, J., Fengs, X., Zhang, N., & Peng, J. (2018). An improved collaborative filtering method based on similarity. PLoS ONE. doi:10.1371/journal.pone.0204003

Fkih, F. (2021). Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison. Journal of King Saud University – Computer and Information Sciences. doi:10.1016/j.jksuci.2021.09.014

Ghannadrad, A., Arezoumandan, M., Candela, L., & Castelli, D. (2022). Recommender Systems for Science: A Basic Taxonomy. CEUR Workshop Proceedings.

Hariadi, A. I., & Nurjanah, D. (2018). Hybrid attribute and personality based recommender system for book recommendation. Proceedings of 2017 International Conference on Data and Software Engineering, ICoDSE 2017. doi:10.1109/ICODSE.2017.8285874

Hikmatyar, M., & Ruuhwan. (2020). Book Recommendation System Development Using User-Based Collaborative Filtering. Journal of Physics: Conference Series. doi:10.1088/1742-6596/1477/3/032024

Logesh, R., Subramaniyaswamy, V., Malathi, D., Sivaramakrishnan, N., & Vijayakumar, V. (2018). Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Springer Nature. doi:10.1007/s00521-018-3891-5 ORIGINAL ARTICLE Enhancing

Mahmud, F. G., Hermanto, T. I., & Nugroho, I. M. (2023). IMPLEMENTATION OF K-NEAREST NEIGHBOR ALGORITHM WITH SMOTE FOR HOTEL REVIEWS SENTIMENT ANALYSIS. SinkrOn, 8(2), 595–602. doi:: https://doi.org/10.33395/10.33395/sinkron.v8i2.12214

Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book Recommendation System through content based and collaborative filtering method. Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016. doi:10.1109/SAPIENCE.2016.7684166

Ng, Y. K. (2020). CBREC: A book recommendation system for children using the matrix factorisation and content-based filtering approaches. International Journal of Business Intelligence and Data Mining. doi:10.1504/IJBIDM.2020.104738

Pujahari, A., & Sisodia, D. S. (2019). Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems. Information Sciences. doi:10.1016/j.ins.2019.03.064

Rana, A., & Deeba, K. (2019). Online book recommendation system using collaborative filtering (with jaccard similarity). Journal of Physics: Conference Series. doi:10.1088/1742-6596/1362/1/012130

Resmi, M. G., Hermanto, T. I., & Ghozali, M. Al. (2022). Product Recommendation System Application Using Web-Based Equivalence Class Transformation (Eclat) algorithm. SinkrOn, 7(3), 957–961. doi:https://doi.org/10.33395/sinkron.v7i3.11454

Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender Systems Handbook. (F. Ricci, L. Rokach, & B. Shapira, Eds.) (Third). Springer Science. doi:10.1007/978-1-0716-2197-4

Saeed, A. A. M., & Taqa, A. Y. (2022). A proposed approach for plagiarism detection in Article documents. SinkrOn, 7(2), 568–578. doi:10.33395/sinkron.v7i2.11381

Saleh, A., Dharshinni, N., Perangin-Angin, D., Azmi, F., & Sarif, M. I. (2023). Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm. Sinkron. doi:10.33395/sinkron.v8i1.11954

Sang Nguyen, T. T. (2019). Model-based book recommender systems using Naïve Bayes enhanced with optimal feature selection. ACM International Conference Proceeding Series. doi:10.1145/3316615.3316727

Sarma, D., Mittra, T., & Hossain, S. (2021). Personalized Book Recommendation System using Machine Learning Algorithm. International Journal of Advanced Computer Science and Applications. doi:10.14569/IJACSA.2021.0120126

Shuxian, L., & Sen, F. (2019). Design and Implementation of Movie Recommendation System Based on Naive Bayes. Journal of Physics: Conference Series, 1345(4). doi:10.1088/1742-6596/1345/4/042042

Suryakant, & Mahara, T. (2016). A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment. Elsevier B.V. doi:: 10.1016/j.procs.2016.06.099

Sütçü, M., KAYA, E., & ERDEM, O. (2021). Movie Recommendation Systems Based on Collaborative Filtering : A Case Study on Netflix İşbirlikçi Filtreleme Temelinde Film Öneri Sistemleri : Netflix. Journal of Institue Of Science and Technology, 37, 367–376.

Tewaria, A. S. (2020). Generating Items Recommendations by Fusing Content and User- Item based Collaborative Filtering Item based Collaborative Filtering. Elsevier B.V., 167, 1934–1940. doi:10.1016/j.procs.2020.03.215

Xia, H., Li, J. J., & Liu, Y. (2020). Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning. IEEE Access, 8, 208149–208157. doi:10.1109/ACCESS.2020.3038770

Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving Recommendation Lists Through Topic Diversification. Chiba, Japan: International World Wide Web Conference Com- mittee (IW3C2).


Crossmark Updates

How to Cite

Saleh, A. M. ., & Taqa , A. Y. . (2023). A proposed User-Based Approach for eBooks Recommendation Using a Weighted Nearest Neighbor Technique. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1316-1325. https://doi.org/10.33395/sinkron.v8i3.12441