@Article{ujpr429,
AUTHOR = {Marappan, Raja and Bhaskaran, S.},
TITLE = {The Advances in Recommendation Systems – Theoretical Analysis},
JOURNAL = {Universal Journal of Physics Research},
VOLUME = {1},
YEAR = {2022},
NUMBER = {2},
PAGES = {52-55},
URL = {https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/429},
ISSN = {2834-5479},
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.},
}

%0 Journal Article
%A Marappan, Raja
%A Bhaskaran, S.
%D 2022
%J Universal Journal of Physics Research

%@ 2834-5479
%V 1
%N 2
%P 52-55

%T The Advances in Recommendation Systems – Theoretical Analysis
%M doi:10.31586/ijmebac.2022.429
%U https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/429

TY  - JOUR
AU  - Marappan, Raja
AU  - Bhaskaran, S.
TI  - The Advances in Recommendation Systems – Theoretical Analysis
T2  - Universal Journal of Physics Research
PY  - 2022
VL  - 1
IS  - 2
SN  - 2834-5479
SP  - 52
EP  - 55
UR  - https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/429
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
ER  -