Implementation of Dimensionality Reduction with SVD to Improve Rating Prediction in Recommender System
- M. Naufal Mu'afa Telkom University, Bandung, Indonesia
-
Z.K.A. Baizal
*
Telkom University, Bandung,
Indonesia
(*) Corresponding Author
Abstract
Recommender system is widely implemented in various fields. Collaborative Filtering is one of the most used recommender system paradigms because it is easy to use. K-means clustering algorithm is widely use in Collaborative Filtering. This algorithm can predict the item rating that will be given by a user. Rating can be predicted by calculating the average rating of the item. The clustering performance of this algorithm is low because this algorithm selects initial centroid randomly. This causes high errors in the item rating prediction. To obtain lower error, we propose dimensionality reduction with Singular Value Decomposition (SVD). SVD is able to factorize the clustering result data and reduce dimensionality of the data. Dimensionality reduction with SVD can be carried out by removing non-dominant characteristics of the data. This study uses the result of factorization to calculate the similarity between clusters. The value of similarity between clusters is used to predict the rating of an item that will be given by a cluster. The experimental results show that the combined method of K-means and SVD can produces RMSE up to 8.936% lower than the K-means method.
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References
Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Computational model for generating interactions in conversational recommender system based on product functional requirements,” Data and Knowledge Engineering, vol. 128, 2020, doi: 10.1016/j.datak.2020.101813.
M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, “Two recommendation system algorithms used SVD and association rule on implicit and explicit data sets,” International Journal of Scientific and Technology Research, vol. 9, no. 1, 2020.
N. Salem and S. Hussein, “Data dimensional reduction and principal components analysis,” in Procedia Computer Science, 2019, vol. 163. doi: 10.1016/j.procs.2019.12.111.
R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, 2020, doi: 10.38094/jastt1224.
H. Zarzour, Z. Al-Sharif, M. Al-Ayyoub, and Y. Jararweh, “A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques,” in 2018 9th International Conference on Information and Communication Systems, ICICS 2018, 2018, vol. 2018-January. doi: 10.1109/IACS.2018.8355449.
R. Barathy and P. Chitra, “Applying Matrix Factorization in Collaborative Filtering Recommender Systems,” 2020. doi: 10.1109/ICACCS48705.2020.9074227.
M. Nilashi, O. Ibrahim, and K. Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” Expert Systems with Applications, vol. 92, 2018, doi: 10.1016/j.eswa.2017.09.058.
J. Wang, P. Han, Y. Miao, and F. Zhang, “A Collaborative Filtering Algorithm Based on SVD and Trust Factor,” 2019. doi: 10.2991/cnci-19.2019.5.
R. Ahuja, A. Solanki, and A. Nayyar, “Movie recommender system using k-means clustering and k-nearest neighbor,” 2019. doi: 10.1109/CONFLUENCE.2019.8776969.
F. Anowar, S. Sadaoui, and B. Selim, “Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE),” Computer Science Review, vol. 40. 2021. doi: 10.1016/j.cosrev.2021.100378.
S. M. Atif, S. Qazi, and N. Gillis, “Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction,” Pattern Recognition Letters, vol. 122, 2019, doi: 10.1016/j.patrec.2019.02.018.
G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System,” in Journal of Physics: Conference Series, 2018, vol. 1000, no. 1. doi: 10.1088/1742-6596/1000/1/012101.
A. Davoudi and M. Chatterjee, “Social trust model for rating prediction in recommender systems: Effects of similarity, centrality, and social ties,” Online Social Networks and Media, vol. 7, 2018, doi: 10.1016/j.osnem.2018.05.001.
J. Zhang, Y. Wang, Z. Yuan, and Q. Jin, “Personalized real-time movie recommendation system: Practical prototype and evaluation,” Tsinghua Science and Technology, vol. 25, no. 2, 2020, doi: 10.26599/TST.2018.9010118.
A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70/30 or 80/20 Relation Between Training and Testing Sets : A Pedagogical Explanation,” Departmental Technical Reports (CS), 2018.
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