Authors:
Nisha Bali,Kulvinder Singh,Sanjeev Dhawan,DOI NO:
https://doi.org/10.26782/jmcms.2025.02.00009Keywords:
Collaborative filtering,Recommendation system,user-based,item-based,Model Comparison,Abstract
Recommendation systems are very crucial for enhancing the utility of a given product and or service for a specific user in different fields. This paper focuses on the comparison of various filtering techniques in order to determine their effectiveness in identifying the preferences of the user. The paper also looks into basic methods like user-based collaborative filtering and item-based collaborative filtering, which uses the item's attributes. Also, the paper assesses the subsequent methods such as linear regression, ridge regression, Lasso regression, random forest regression, and XGBoost regression. From the performance evaluation metrics, the researchers reach RMSE and MAE to compare the effectiveness of the proposed methods and reveal their weaknesses. This paper aims to evaluate the performances of the above filtering approaches to gain an understanding of the extent to which these methods improve the recommendations' accuracy and contribute to the literature by providing recommendations on filtering models suitable for various recommendation tasks.Refference:
I. Al-Ghobari, M., Muneer, A., & Fati, S. M. (2021). Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN. Computers, Materials & Continua/Computers, Materials & Continua (Print), 69(2), 1553–1570. 10.32604/cmc.2021.016348
II. Alhijawi, B., Al-Naymat, G., Obeid, N., & Awajan, A. (2021). Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Information Systems, 96, 101670. 10.1016/j.is.2020.101670
III. Alcacer, A., Epifanio, I., Valero, J., & Ballester, A. (2021). Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size. Mathematics, 9(7), 771. 10.3390/math9070771
IV. Aljunid, M. F., & Huchaiah, M. D. (2021). An efficient hybrid recommendation model based on collaborative filtering recommender systems. CAAI Transactions on Intelligence Technology, 6(4), 480–492. 10.1049/cit2.12048
V. Anwar, T., & Uma, V. (2021). Comparative study of recommender system approaches and movie recommendation using collaborative filtering. International Journal of Systems Assurance Engineering and Management, 12(3), 426–436. 10.1007/s13198-021-01087-x
VI. Chen, C., Zhang, M., Zhang, Y., Ma, W., Liu, Y., & Ma, S. (2020). Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 19–26. 10.1609/aaai.v34i01.5329
VII. Dang, C. N., Moreno-García, M. N., & De La Prieta, F. (2021). An Approach to Integrating Sentiment Analysis into Recommender Systems. Sensors, 21(16), 5666. 10.3390/s21165666
VIII. Fang, J., Li, B., & Gao, M. (2020). Collaborative filtering recommendation algorithm based on deep neural network fusion. International Journal of Sensor Networks, 34(2), 71. 10.1504/ijsnet.2020.110460
IX. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences, 10(21), 7748. 10.3390/app10217748
X. Forouzandeh, S., Berahmand, K., & Rostami, M. (2020). Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens. Multimedia Tools and Applications, 80(5), 7805–7832. 10.1007/s11042-020-09949-5
XI. Huang, L., Guan, C. R., Huang, Z. W., Gao, Y., Wang, C. D., & Chen, C. L. P. (2024). Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach. IEEE Transactions on Emerging Topics in Computational Intelligence, 1–15. 10.1109/tetci.2024.3378599
XII. Iwendi, C., Ibeke, E., Eggoni, H., Velagala, S., & Srivastava, G. (2021). 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. 10.1142/s0219622021500619
XIII. K, R. C., & Srikantaiah, K. (2021). Similarity Based Collaborative Filtering Model for Movie Recommendation Systems. 10.1109/iciccs51141.2021.9432354
XIV. Kim, T. Y., Ko, H., Kim, S. H., & Kim, H. D. (2021). Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors, 21(6), 1997. 10.3390/s21061997
XV. Mohamed, M. H., Khafagy, M. H., & Ibrahim, M. H. (2019). Recommender Systems Challenges and Solutions Survey. https://doi.org/10.1109/itce.2019.8646645
XVI. Mu, Y., & Wu, Y. (2023). Multimodal Movie Recommendation System Using Deep Learning. Mathematics, 11(4), 895. 10.3390/math11040895
XVII. Nassar, N., Jafar, A., & Rahhal, Y. (2020). Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00309-6
XVIII. Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A. H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Systems With Applications, 149, 113248. 10.1016/j.eswa.2020.113248
XIX. Nguyen, L. V., Hong, M. S., Jung, J. J., & Sohn, B. S. (2020). Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences, 10(12), 4183. 10.3390/app10124183
XX. Nguyen, L. V., Vo, Q. T., & Nguyen, T. H. (2023). Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services. Big Data and Cognitive Computing, 7(2), 106. 10.3390/bdcc7020106
XXI. Papadakis, H., Papagrigoriou, A., Panagiotakis, C., Kosmas, E., & Fragopoulou, P. (2022). Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems, 64(1), 35–74. 10.1007/s10115-021-01628-7
XXII. Peng, S., Siet, S., Ilkhomjon, S., Kim, D. Y., & Park, D. S. (2024). Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation Systems. Applied Sciences, 14(3), 1155. 10.3390/app14031155
XXIII. R. K., & S, M. J. A. (2021). A Hybrid Deep Collaborative Filtering Approach for Recommender Systems. Research Square (Research Square). 10.21203/rs.3.rs-651522/v1
XXIV. Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1). 10.1186/s40537-022-00592-5
XXV. Sharma, S., Rana, V., & Malhotra, M. (2021). Automatic recommendation system based on hybrid filtering algorithm. Education and Information Technologies. 10.1007/s10639-021-10643-8
XXVI. Shokrzadeh, Z., Feizi-Derakhshi, M. R., Balafar, M. A., & Mohasefi, J. B. (2024). Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding. Ain Shams Engineering Journal/Ain Shams Engineering Journal, 15(1), 102263. 10.1016/j.asej.2023.102263
XXVII. Singh, P. K., Sinha, M., Das, S., & Choudhury, P. (2020). Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item. Applied Intelligence, 50(12), 4708–4731. 10.1007/s10489-020-01775-4
XXVIII. 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. 10.1007/s11042-021-10965-2
XXIX. Widiyaningtyas, T., Hidayah, I., & Adji, T. B. (2021). User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. Journal of Big Data, 8(1). 10.1186/s40537-021-00425-x
XXX. Xue, F., He, X., Wang, X., Xu, J., Liu, K., & Hong, R. (2019). Deep Item-based Collaborative Filtering for Top-N Recommendation. ACM Transactions on Office Information Systems, 37(3), 1–25. 10.1145/3314578
XXXI. Yalcin, E., & Bilge, A. (2024). A novel target item-based similarity function in privacy-preserving collaborative filtering. The Journal of Supercomputing. 10.1007/s11227-024-06221-7
XXXII. Yu. S., Guo, M., Chen, X., Qiu, J., & Sun, J. (2023). Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks. Mathematics, 11(6), 1355. 10.3390/math11061355
XXXIII. Zhou, K., Yu, H., Zhao, W. X., & Wen, J. R. (2022). Filter-enhanced MLP is All You Need for Sequential Recommendation. Proceedings of the ACM Web Conference 2022. 10.1145/3485447.3512111