Authors :
Koppadi. Bhavani; Kottu. Aslesha Lakshmi Sai
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/4nervjvy
Scribd :
https://tinyurl.com/4dmm97ex
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB1527
Abstract :
Netflix is a leading global entertainment
company with over 247 million paying customers who
can access TV series, films, and games in over 190+
countries. Many languages and genres are offered for the
content. As much as they like, whenever and wherever
they wish, participants are free to change their plans and
play, pause, and resume viewing.
With the exponential growth of content on
streaming platforms like Netflix, users often face the
challenge of finding relevant and enjoyable movies.The
"Cold-Start" issue is a recommendation system issue
wherein new users or items have little data, making it
challenging to provide appropriate recommendations.
In response, this paper proposes a data-driven
movie recommendation system tailored for Netflix.
Leveraging machine learning techniques, user
preferences, and historical data, the model aims to
enhance the user experience by providing personalized
movierecommendations. This study explores algorithms
such as collaborative filtering, content-based filtering,
and hybrid models to achieve accurate and effective
movie recommendations.
While content-based filtering uses item features to
suggest shows or movies a user has previously enjoyed,
collaborative filtering examines user behavior and
preferences to produce suggestions. Even with
insufficient data, the system can deliver more accurate
recommendations for new users or things by combining
various approaches. By making recommendations based
on both item features and user behavior, this hybrid
approach helps to reduce the cold start issue.
Keywords :
Collaborative Filtering, Content-Based Filtering, Hybrid Models, Movie Recommendation Systems, Machine Learning.
Netflix is a leading global entertainment
company with over 247 million paying customers who
can access TV series, films, and games in over 190+
countries. Many languages and genres are offered for the
content. As much as they like, whenever and wherever
they wish, participants are free to change their plans and
play, pause, and resume viewing.
With the exponential growth of content on
streaming platforms like Netflix, users often face the
challenge of finding relevant and enjoyable movies.The
"Cold-Start" issue is a recommendation system issue
wherein new users or items have little data, making it
challenging to provide appropriate recommendations.
In response, this paper proposes a data-driven
movie recommendation system tailored for Netflix.
Leveraging machine learning techniques, user
preferences, and historical data, the model aims to
enhance the user experience by providing personalized
movierecommendations. This study explores algorithms
such as collaborative filtering, content-based filtering,
and hybrid models to achieve accurate and effective
movie recommendations.
While content-based filtering uses item features to
suggest shows or movies a user has previously enjoyed,
collaborative filtering examines user behavior and
preferences to produce suggestions. Even with
insufficient data, the system can deliver more accurate
recommendations for new users or things by combining
various approaches. By making recommendations based
on both item features and user behavior, this hybrid
approach helps to reduce the cold start issue.
Keywords :
Collaborative Filtering, Content-Based Filtering, Hybrid Models, Movie Recommendation Systems, Machine Learning.
