Exploring collaborative filtering through K-Nearest Neighbors and Non-Negative Matrix Factorization

Sagedur Rahman

doi:10.47813/2782-2818-2024-4-2-0201-0211

Authors

Sagedur Rahman

DOI:

https://doi.org/10.47813/2782-2818-2024-4-2-0201-0211

Keywords:

collaborative filtering, KNN, NMF, recommendation system.

Abstract

Collaborative filtering (CF) algorithms have received a lot of interest in recommender systems due to their ability to give personalized recommendations by exploiting user-item interaction data. In this article, we explore two popular CF methods—K-Nearest Neighbors (KNN) Regression and Non-Negative Matrix Factorization (NMF)—in detail as we dig into the world of collaborative filtering. Our goal is to evaluate their performance on the MovieLens 1M dataset and offer information about their advantages and disadvantages. A thorough explanation of the significance of recommender systems in contemporary content consumption settings is given at the outset of our examination. We look into Collaborative Filtering's complexities and how it uses user choices to produce tailored recommendations. Then, after setting the scene, we explain the KNN Regression and NMF approaches, going over their guiding principles and how they apply to recommendation systems. We conduct an extensive investigation of KNN Regression and NMF on the MovieLens 1M dataset to provide a thorough evaluation. We describe the model training processes, performance measures, and data pre-processing steps used. We measure and analyse the predicted accuracy of these strategies using empirical studies, revealing light on their effectiveness when applied to various user preferences and content categories.

Author Biography

Sagedur Rahman

Sagedur Rahman, Department of Management Science and Engineering, Chongqing University of Posts and Telecommunications, 2^nd Chongwen Road, Nanan District, Nanshan, ,400065, Chongqing, China

References

Resnick P., Varian H. R. Recommender systems. Communications of the ACM. 1997; 40(3): 56–58. https://doi.org/10.1145/245108.245121 DOI: https://doi.org/10.1145/245108.245121

Su X., Khoshgoftaar T. M. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence. 2009; 1–19. https://doi.org/10.1155/2009/421425 DOI: https://doi.org/10.1155/2009/421425

Zhang S., Yao L., Sun A., Tay Y. Deep Learning Based Recommender System. ACM Computing Surveys. 2019; 52(1): 1–38. https://doi.org/10.1145/3285029 DOI: https://doi.org/10.1145/3285029

Adomavicius G., Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering. 2005; 17(6): 734–749. https://doi.org/10.1109/tkde.2005.99 DOI: https://doi.org/10.1109/TKDE.2005.99

Sarwar B., Karypis G., Konstan J., Riedl J. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 2001. https://doi.org/10.1145/371920.372071 DOI: https://doi.org/10.1145/371920.372071

Zhang Z., Peng T., Shen K. Overview of Collaborative Filtering Recommendation Algorithms. IOP Conference Series: Earth and Environmental Science 2020; 440(2): 022063. https://doi.org/10.1088/1755-1315/440/2/022063 DOI: https://doi.org/10.1088/1755-1315/440/2/022063

Zhou T., Kuscsik Z., Liu J. G., Medo M., Wakeling J. R., Zhang Y. C. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 2010; 107(10): 4511–4515. https://doi.org/10.1073/pnas.1000488107 DOI: https://doi.org/10.1073/pnas.1000488107

Herlocker J. L., Konstan J. A., Borchers A., Riedl J. An Algorithmic Framework for Performing Collaborative Filtering. ACM SIGIR Forum. 2017; 51(2): 227–234. https://doi.org/10.1145/3130348.3130372 DOI: https://doi.org/10.1145/3130348.3130372

Yin N. A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms. Open Physics. 2019; 17(1): 966–974. https://doi.org/10.1515/phys-2019-0102 DOI: https://doi.org/10.1515/phys-2019-0102

Lee D. D., Seung H. S. Learning the parts of objects by non-negative matrix factorization. Nature. 1999; 401(6755): 788–791. https://doi.org/10.1038/44565 DOI: https://doi.org/10.1038/44565

Gillis N., Rajkó R. Partial Identifiability for Nonnegative Matrix Factorization. SIAM Journal on Matrix Analysis and Applications. 2023; 44(1): 27–52. https://doi.org/10.1137/22m1507553 DOI: https://doi.org/10.1137/22M1507553

Koren Y., Bell R., Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009; 42(8): 30-37. https://doi.org/10.1109/MC.2009.263 DOI: https://doi.org/10.1109/MC.2009.263