Exploring Matrix Decomposition Methods for Recommender Systems
DOI:
https://doi.org/10.3329/jsr.v16i3.70831Abstract
Matrix factorization, a pivotal technique in recommender systems, is used to uncover latent patterns within user-item interaction matrices. This technique, which reduces the user-product rating matrix into separate lower dimensional matrices, is instrumental in generating personalized recommendations. Our study explores three matrix factorization techniques: U.V. decomposition, singular value decomposition, and the CUR algorithm. We perform a rigorous experimental evaluation on two real-world datasets, ensuring the thoroughness and reliability of our research. We perform a rigorous experimental evaluation to compare the three techniques on two datasets. This provides the thoroughness and reliability of the experimentation.
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Articles published in the "Journal of Scientific Research" are Open Access articles under a Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). This license permits use, distribution and reproduction in any medium, provided the original work is properly cited and initial publication in this journal. In addition to that, users must provide a link to the license, indicate if changes are made and distribute using the same license as original if the original content has been remixed, transformed or built upon.