@Article{crph291,
AUTHOR = {Marappan, Raja and Bhaskaran, S.},
TITLE = {Movie Recommendation System Modeling Using Machine Learning},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2022},
NUMBER = {1},
PAGES = {12-16},
URL = {https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/291},
ISSN = {2831-5162},
DOI = {10.31586/ijmebac.2022.291},
ABSTRACT = {The task of recommending products to customers based on their interests is important in business. It is possible to accomplish this with machine learning. To reduce human effort by proposing movies based on the user's interests efficiently and effectively without wasting much time in pointless browsing, the movie recommendation system is designed to assist movie aficionados. This work focuses on developing a movie recommender system using a model that incorporates both cosine similarity and sentiment analysis. Cosine similarity is a standard used to determine how similar two items are to one another. An examination of the emotions expressed in a movie review can determine how excellent or negative a review is and, consequently the overall rating for a film. As a result, determining whether a review is favorable or adverse may be automated because the machine learns by training and evaluating the data. Comparing different systems based on content-based approaches will produce results that are increasingly explicit as time passes.},
}

%0 Journal Article
%A Marappan, Raja
%A Bhaskaran, S.
%D 2022
%J Current Research in Public Health

%@ 2831-5162
%V 1
%N 1
%P 12-16

%T Movie Recommendation System Modeling Using Machine Learning
%M doi:10.31586/ijmebac.2022.291
%U https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/291

TY  - JOUR
AU  - Marappan, Raja
AU  - Bhaskaran, S.
TI  - Movie Recommendation System Modeling Using Machine Learning
T2  - Current Research in Public Health
PY  - 2022
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 12
EP  - 16
UR  - https://www.scipublications.com/journal/index.php/IJMEBAC/article/view/291
AB  - The task of recommending products to customers based on their interests is important in business. It is possible to accomplish this with machine learning. To reduce human effort by proposing movies based on the user's interests efficiently and effectively without wasting much time in pointless browsing, the movie recommendation system is designed to assist movie aficionados. This work focuses on developing a movie recommender system using a model that incorporates both cosine similarity and sentiment analysis. Cosine similarity is a standard used to determine how similar two items are to one another. An examination of the emotions expressed in a movie review can determine how excellent or negative a review is and, consequently the overall rating for a film. As a result, determining whether a review is favorable or adverse may be automated because the machine learns by training and evaluating the data. Comparing different systems based on content-based approaches will produce results that are increasingly explicit as time passes.
DO  - Movie Recommendation System Modeling Using Machine Learning
TI  - 10.31586/ijmebac.2022.291
ER  -