Authors :
Chaitanya. G; Hemanth. Y; Koushik. K; Haneef. P; Pranav.S
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
http://tinyurl.com/w5a2nzpf
Scribd :
http://tinyurl.com/yks4rssd
DOI :
https://doi.org/10.5281/zenodo.10686713
Abstract :
In this abstract, we present a cutting- edge
movie recommendation system that combines the power
of machine learning algorithms with the scalability and
speed of the Spark framework. Our system is designed to
deliver highly accurate and personalized movie
recommendations to users by analyzing their viewing
history, preferences, and demographic information. By
leveraging Spark's distributed computing capabilities,
we efficiently process large-scale movie datasets and
train complex recommendation models in parallel. The
results of our experiments demonstrate the system's
superior recommendation performance, outperforming
traditional approaches and providing users with a
delightful movie-watching experience.
Keywords :
Movie recommendation system, machine learning, Spark, personalized recommendations, demographic information,distributed computing, scalability.
In this abstract, we present a cutting- edge
movie recommendation system that combines the power
of machine learning algorithms with the scalability and
speed of the Spark framework. Our system is designed to
deliver highly accurate and personalized movie
recommendations to users by analyzing their viewing
history, preferences, and demographic information. By
leveraging Spark's distributed computing capabilities,
we efficiently process large-scale movie datasets and
train complex recommendation models in parallel. The
results of our experiments demonstrate the system's
superior recommendation performance, outperforming
traditional approaches and providing users with a
delightful movie-watching experience.
Keywords :
Movie recommendation system, machine learning, Spark, personalized recommendations, demographic information,distributed computing, scalability.
