Enhancing Product Recommendation Systems Using Hybrid Filtering: A Comparative Analysis of Collaborative and Content-Based Approaches
Keywords:
Hybrid Recommendation System, Collaborative Filtering, Content-Based Filtering, Product Recommendation, PersonalizatioAbstract
The rapid growth of e-commerce platforms has led to an overwhelming number of product choices, creating challenges for users in identifying items that match their preferences. Recommendation systems have become essential tools to address this issue; however, traditional approaches such as Collaborative Filtering and Content-Based Filtering suffer from limitations including data sparsity, cold-start problems, and limited recommendation diversity. This study proposes a Hybrid Filtering-based product recommendation system that integrates both Collaborative Filtering and Content-Based Filtering techniques to overcome these challenges. The proposed model utilizes user-item interaction data and product metadata to generate personalized recommendations through a hybrid approach, combining algorithms such as K-Nearest Neighbors (KNN), Matrix Factorization, Term Frequency–Inverse Document Frequency (TF-IDF), and cosine similarity. The system is evaluated using multiple performance metrics, including accuracy (precision, recall, and F1-score), ranking quality (Mean Average Precision and Normalized Discounted Cumulative Gain), and prediction error (Root Mean Square Error and Mean Absolute Error). The results demonstrate that the Hybrid Filtering model outperforms individual methods in all evaluation aspects. It achieves higher accuracy, better ranking performance, lower prediction error, and greater diversity in recommendations. These findings indicate that the hybrid approach effectively addresses the limitations of traditional recommendation systems and provides more reliable and personalized recommendations. In conclusion, this research confirms that Hybrid Filtering is a robust and efficient method for improving the performance of product recommendation systems. The proposed model has significant practical implications for e-commerce platforms, as it enhances user experience, increases engagement, and supports better decision-making processes.
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Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M. S. H., Dipta, D. R., & Hossain, E. (2020). A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access, 8, 134911–134939.
Alzoghbi, A., Arrascue Ayala, V. A., Fischer, P. M., & Lausen, G. (2016). Learning-to-Rank in research paper CBF recommendation: Leveraging irrelevant papers.
Chen, R., Hua, Q., Chang, Y.-S., Wang, B., Zhang, L., & Kong, X. (2018). A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, 6, 64301–64320.
Chen, Y.-C., Pal, N. R., & Chung, I.-F. (2011). An integrated mechanism for feature selection and fuzzy rule extraction for classification. IEEE Transactions on Fuzzy Systems, 20(4), 683–698.
De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, 51(7), 785–799.
Elavarasan, D., Vincent PM, D. R., Srinivasan, K., & Chang, C.-Y. (2020). A hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling. Agriculture, 10(9), 400.
Galea, A., & Capelo, L. (2018). Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions. Packt Publishing Ltd.
Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conference Series, 1000(1), 12101.
Gupta, D., Chopra, N., Nair, N., & Sharma, P. (2020). Enhancing User Experience with AI-Powered Recommendation Engines: A Comparative Study of Collaborative Filtering, Neural Collaborative Filtering, and Matrix Factorization Algorithms. Journal of AI ML Research, 9(4).
Jawaheer, G., Weller, P., & Kostkova, P. (2014). Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. ACM Transactions on Interactive Intelligent Systems (TiiS), 4(2), 1–26.
Joshi, A. P., & Patel, B. V. (2021). Data preprocessing: the techniques for preparing clean and quality data for data analytics process. Orient. J. Comput. Sci. Technol, 13(0203), 78–81.
Kim, B. M., Li, Q., Park, C. S., Kim, S. G., & Kim, J. Y. (2006). A new approach for combining content-based and collaborative filters. Journal of Intelligent Information Systems, 27(1), 79–91.
Lai, C.-H., & Hsu, C.-Y. (2021). Rating prediction based on combination of review mining and user preference analysis. Information Systems, 99, 101742.
Madni, A. M., & Sievers, M. (2014). System of systems integration: Key considerations and challenges. Systems Engineering, 17(3), 330–347.
Mehdiyev, N., Enke, D., Fettke, P., & Loos, P. (2016). Evaluating forecasting methods by considering different accuracy measures. Procedia Computer Science, 95, 264–271.
Molina, L. C., Belanche, L., & Nebot, À. (2002). Feature selection algorithms: A survey and experimental evaluation. 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 306–313.
Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., & Konstan, J. A. (2014). Exploring the filter bubble: the effect of using recommender systems on content diversity. Proceedings of the 23rd International Conference on World Wide Web, 677–686.
Olaolu, A. M., Abdulsalam, S. O., Mope, I. R., & Kazeem, G. A. (2018). A comparative analysis of feature selection and feature extraction models for classifying microarray dataset. Comput Inf Syst J, 29(1).
Qaid, T. S., Mazaar, H., Al-Shamri, M. Y. H., Alqahtani, M. S., Raweh, A. A., & Alakwaa, W. (2021). Hybrid deep?learning and machine?learning models for predicting COVID?19. Computational Intelligence and Neuroscience, 2021(1), 9996737.
Rosário, A., & Raimundo, R. (2021). Consumer marketing strategy and E-commerce in the last decade: a literature review. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3003–3024.
Shi, Y., Larson, M., & Hanjalic, A. (2014). Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1), 1–45.
Shrivastava, R., & Sisodia, D. S. (2019). Product recommendations using textual similarity based learning models. 2019 International Conference on Computer Communication and Informatics (ICCCI), 1–7.
Sujatha, E., & Radha, R. (2021). A Hybrid of Proposed Filtration and Feature Selections to Enhance the Model Performance. Indian Journal of Science and Technology, 14(24), 2039–2050.
Valizadegan, H., Jin, R., Zhang, R., & Mao, J. (2009). Learning to rank by optimizing ndcg measure. Advances in Neural Information Processing Systems, 22.
Verma, C., Hart, M., Bhatkar, S., Parker-Wood, A., & Dey, S. (2015). Improving scalability of personalized recommendation systems for enterprise knowledge workers. IEEE Access, 4, 204–215.
Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91.
Zheng, L. (2015). Performance evaluation of latent factor models for rating prediction.
Zheng, X., Cheung, C. M. K., Lee, M. K. O., & Liang, L. (2015). Building brand loyalty through user engagement in online brand communities in social networking sites. Information Technology & People, 28(1), 90–106.
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