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DOI: 10.14569/IJACSA.2021.0120367
PDF

Evaluation of Collaborative Filtering for Recommender Systems

Author 1: Maryam Al-Ghamdi
Author 2: Hanan Elazhary
Author 3: Aalaa Mojahed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 3, 2021.

  • Abstract and Keywords
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Abstract: Recently, due to the increasing amount of data on the Internet along with the increase in products’ purchasing via e-commerce websites, Recommender Systems (RS) play an important role in guiding customers to buy products they may prefer. Furthermore, these systems help the companies to advertise their products to the most potential customers, and therefore raise their revenues. Collaborative Filtering (CF) is the most popular RS approach. It is classified into memory-based and model-based filtering. Memory-based filtering is in turn classified into user-based and item-based. Several algorithms have been proposed for CF. In this paper, a comparison has been performed between different CF algorithms to assess their performance. Specifically, we evaluated K-Nearest Neighbor (KNN), Slope One, co-clustering and Non-negative Matrix Factorization (NMF) algorithms. KNN algorithm is representative of the memory-based CF approach (both user-based and item-based). The other three algorithms, on the other hand, are under the model-based CF approach. In our experiments, we used a popular MovieLens dataset based on six evaluation metrics. Our results reveal that the KNN algorithm for item-based CF outperformed all other algorithms examined in this paper.

Keywords: Co-clustering; collaborative filtering; KNN; NMF; recommender systems; slope one

Maryam Al-Ghamdi, Hanan Elazhary and Aalaa Mojahed, “Evaluation of Collaborative Filtering for Recommender Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120367

@article{Al-Ghamdi2021,
title = {Evaluation of Collaborative Filtering for Recommender Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120367},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120367},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Maryam Al-Ghamdi and Hanan Elazhary and Aalaa Mojahed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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