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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM23278

A Comparative Analysis of Machine Learning Algorithms for Recommender System

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM23278

Volume:

7 |

6 |

Jun 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

OD

Omprakash Dewangan

KS

Karan Sharma

AG

Ayush Gupta

YKS

Yogesh Kumar Sahu

VKS

Vijay Kumar Sahu

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Abstract

In recent years, Machine Learning Algorithms (MLA) has become increasingly popular for use in Recommender Systems (RS). RS are used to generate personalized recommendations for users based on their preferences and past behavior. MLA offer several advantages over traditional methods of RS, such as improved accuracy, scalability, and robustness. This review paper aims to provide a comparative analysis of the various MLA used in RS, examining the strengths and weaknesses of each.

KEYWORDS

Collaborative Filtering Content-Based Filtering Latent Factor Models Machine Learning Recommender System

Cite This Article

Omprakash Dewangan, Karan Sharma, Ayush Gupta, Yogesh Kumar Sahu, Vijay Kumar Sahu, "A Comparative Analysis of Machine Learning Algorithms for Recommender System," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 6, Jun 2023. https://doi.org/10.55041/IJSREM23278

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Publication Date

11/5/2023

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