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Review Article Open Access September 07, 2022

The Advances in Recommendation Systems – Theoretical Analysis

Raja Marappan 1,* and S. Bhaskaran 1
1
School of Computing, SASTRA Deemed University, Thanjavur, India
Publihed in: Current Research in Public Health (Volume 1, Issue 2, 2022)
Page(s): 52-55
DOI: 10.31586/ijmebac.2022.429
Received
July 29, 2022
Revised
August 28, 2022
Accepted
September 05, 2022
Published
September 07, 2022
Keywords
Recommendation Systems; Decision Making; Viewing Habits; Personalized Recommender; Recommender Analysis
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2022. Published by Scientific Publications
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Cite This Article

APA Style
Marappan, R. , & Bhaskaran, S. (2022). The Advances in Recommendation Systems – Theoretical Analysis. Current Research in Public Health, 1(2), 52-55. https://doi.org/10.31586/ijmebac.2022.429
ACS Style
Marappan, R. ; Bhaskaran, S. The Advances in Recommendation Systems – Theoretical Analysis. Current Research in Public Health 2022 1(2), 52-55. https://doi.org/10.31586/ijmebac.2022.429
Chicago/Turabian Style
Marappan, Raja, and S. Bhaskaran. 2022. "The Advances in Recommendation Systems – Theoretical Analysis". Current Research in Public Health 1, no. 2: 52-55. https://doi.org/10.31586/ijmebac.2022.429
AMA Style
Marappan R, Bhaskaran S. The Advances in Recommendation Systems – Theoretical Analysis. Current Research in Public Health. 2022; 1(2):52-55. https://doi.org/10.31586/ijmebac.2022.429 
@Article{crph429,
AUTHOR = {Marappan, Raja and Bhaskaran, S.},
TITLE = {The Advances in Recommendation Systems – Theoretical Analysis},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2022},
NUMBER = {2},
PAGES = {52-55},
URL = {https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/429},
ISSN = {2831-5162},
DOI = {10.31586/ijmebac.2022.429},
ABSTRACT = {Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.},
}
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AB  - Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.
DO  - The Advances in Recommendation Systems – Theoretical Analysis
TI  - 10.31586/ijmebac.2022.429
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