A proposed User-Based Approach for eBooks Recommendation Using a Weighted Nearest Neighbor Technique
DOI:
10.33395/sinkron.v8i3.12441Keywords:
Book-Crossing, Collaborative filtering, eBooks recommender, KNN with Weight, Pearson correlation, User-BasedAbstract
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.
Downloads
References
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).
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2023 Abdullah Mohammed Saleh, Alaa Yaseen Taqa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
