Online Store Product Recommendation System Using Collaborative Filtering and Content-Based Filtering Algorithms to Increase Sales
Main Article Content
Yosi Afandi
Maskur*
Wahyu Widyananda
Nilawati Fiernaningsih
Lina Budiarti
Fahmi Muhammad Az Zuhri
This study aims to evaluate and compare the performance of two recommendation system approaches, namely Collaborative Filtering (CF) and Content-Based Filtering (CBF), in providing relevant product recommendations to users in an e-commerce context. The dataset used consists of 120 data including 90 relevant and recommended products (True Positive), 20 recommended but irrelevant products (False Positive), and 10 relevant but not recommended products (False Negative). Based on the calculation results, both methods show a precision value of 0.818 and a recall of 0.900. This means that approximately 81.8% of products recommended by the system are truly relevant, while 90% of the total relevant products are successfully recommended to users. The F1-score value obtained of 0.857 illustrates a good balance between the accuracy and completeness of the recommendations generated by the system. Furthermore, to measure the level of rating prediction error, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics are used. The evaluation results show that the CF method has an MSE value of 0.0784 and an RMSE of 0.28, while the CBF method shows an MSE of 0.0961 and an RMSE of 0.31. The lower RMSE value of CF indicates that this method has better accuracy in predicting user preferences than CBF. Overall, both methods show good performance with a low error rate. However, CF proved slightly superior in providing recommendations that match user preferences, so it can be used as a basis for developing smarter and more personalized recommendation systems on e-commerce platforms.
Ajaegbu, C. (2021). An optimized item-based collaborative filtering algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(12), 10629–10636. https://doi.org/10.1007/s12652-020-02876-1
Alshareet, O., & Awasthi, A. (2025). Collaborative filtering in the age of AI: Foundations, innovations, and emerging trends. Computing, 107(11), 216. https://doi.org/10.1007/s00607-025-01564-2
Gulzar, Y., Alwan, A. A., Abdullah, R. M., Abualkishik, A. Z., & Oumrani, M. (2023). OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System. Sustainability, 15(4), 2947. https://doi.org/10.3390/su15042947
Iwendi, C., Ibeke, E., Eggoni, H., Velagala, S., & Srivastava, G. (2022). Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model. International Journal of Information Technology & Decision Making, 21(01), 463–484. https://doi.org/10.1142/S0219622021500619
Jozani, M., Liu, C. Z., & Choo, K.-K. R. (2023). An empirical study of content-based recommendation systems in mobile app markets. Decision Support Systems, 169, 113954. https://doi.org/10.1016/j.dss.2023.113954
Kulvinder Singh, Dhawan, S., & Bali, N. (2024). An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting Algorithms. Automatic Control and Computer Sciences, 58(5), 491–505. https://doi.org/10.3103/S0146411624700615
Lee, J., & Kim, J. (2023). Developing a Convenience Store Product Recommendation System through Store-Based Collaborative Filtering. Applied Sciences, 13(20), 11231. https://doi.org/10.3390/app132011231
Lee, K. J., Hwangbo, Y., Jeong, B., Yoo, J., & Park, K. Y. (2021). Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs. Sustainability, 13(13), 7156. https://doi.org/10.3390/su13137156
Li, L., Zhang, Z., & Zhang, S. (2021). Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation. Scientific Programming, 2021(1), 7427409. https://doi.org/10.1155/2021/7427409
Liao, M., & Sundar, S. S. (2022). When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering. Journal of Advertising, 51(2), 256–267. https://doi.org/10.1080/00913367.2021.1887013
Murty, C. S. V. V. S. N., Saradhi Varma, G. P., & Satyanarayana, Ch. (2022). Content-Based Collaborative Filtering with Hierarchical Agglomerative Clustering Using User/ Item based Ratings. Journal of Interconnection Networks, 22(Supp01), 2141026. https://doi.org/10.1142/S0219265921410267
Nguyen, D.-N., Nguyen, V.-H., Trinh, T., Ho, T., & Le, H.-S. (2024). A personalized product recommendation model in e-commerce based on retrieval strategy. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100303. https://doi.org/10.1016/j.joitmc.2024.100303
Parthasarathy, G., & Sathiya Devi, S. (2023). Hybrid Recommendation System Based on Collaborative and Content-Based Filtering. Cybernetics and Systems, 54(4), 432–453. https://doi.org/10.1080/01969722.2022.2062544
Patoulia, A. A., Kiourtis, A., Mavrogiorgou, A., & Kyriazis, D. (2022). A Comparative Study of Collaborative Filtering in Product Recommendation. Emerging Science Journal, 7(1), 1–15. https://doi.org/10.28991/ESJ-2023-07-01-01
Renukadevi, R., Ramalingam, M., Sathishkumar, K., Kumar, E., S., J., & Kholmanovich, A. (2024). An Improved Collaborative User Product Recommendation System Using Computational Intelligence with Association Rules. Communications on Applied Nonlinear Analysis, 31, 554–564.
Sharma, R. S., Shaikh, A. A., & Li, E. (2021). Designing Recommendation or Suggestion Systems: Looking to the future. Electronic Markets, 31(2), 243–252. https://doi.org/10.1007/s12525-021-00478-z
Sinha, M., & Srivastava, D. (2021). Impact of recommender algorithms on the sales of e-commerce websites. International Journal of Innovation Science, 13(2), 161–174. https://doi.org/10.1108/IJIS-09-2020-0155
Thakker, U., Patel, R., & Shah, M. (2021). A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimedia Tools and Applications, 80(19), 28647–28672. https://doi.org/10.1007/s11042-021-10965-2
Torkashvand, A., Jameii, S. M., & Reza, A. (2023). Deep learning-based collaborative filtering recommender systems: A comprehensive and systematic review. Neural Computing and Applications, 35(35), 24783–24827. https://doi.org/10.1007/s00521-023-08958-3
Xia, H., & Wang, Y. (2025). Enhancing Neural Collaborative Filtering for Product Recommendation by Integrating Sales Data and User Satisfaction. Electronics, 14(16), 3165. https://doi.org/10.3390/electronics14163165
Zhong, M., & Ding, R. (2022). Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering. International Journal of Circuits, Systems and Signal Processing, 16, 122–131. https://doi.org/10.46300/9106.2022.16.16
