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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM34579

Recommender Systems: A Comprehensive Review of Models, Techniques, and Emerging Trends

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM34579

Volume:

8 |

5 |

May 2024

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

MNJ

Ms. Nansi Jain

ASY

Ajeet Singh Yadav

KS

Kanishk Sharma

SD

Shivansh Dubey

SS

Shreyash Shandilya

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Abstract

Recommendation systems’ main goal is to provide a consumer with relevant objects based solely on historical data. A movie that has received a high rating from a customer who has also seen the film you are currently viewing may appear in the suggestions. It’s likely that almost everyone will enjoy the movies with the highest total scores. CineMatch is the name of the algorithm that performs all of these functions. Regarding individual users, it also gains insight from their behavior to enhance the likelihood that they will find a film engaging. Here, we need to apply cutting-edge collaborative filtering approaches to boost our CineMatch algorithm by 10%.

KEYWORDS

Sparse matrix Similarity matrix Movies Ratings Machine learning models.

Cite This Article

Ms. Nansi Jain, Ajeet Singh Yadav, Kanishk Sharma, Shivansh Dubey, Shreyash Shandilya, "Recommender Systems: A Comprehensive Review of Models, Techniques, and Emerging Trends," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024.

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12/31/45436

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Double-Blind Peer Review

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