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Movie Recommendation System Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I5-1712251
Abstract
With the continuous expansion of digital streaming platforms, personalized movie recommendations have become essential to enhance user experience. This paper presents a machine-learning?based movie recommendation system utilizing content-based filtering, collaborative filtering, and hybrid approaches. The system is evaluated using the MovieLens dataset, and performance is measured using RMSE and MAE. The results indicate that hybrid models outperform individual approaches due to their ability to integrate user?item interactions with content features.
Keywords
Recommendation System, Machine Learning, Collaborative Filtering, Content-Based Filtering, Hybrid Models, Movielens Dataset.
How to cite this paper
@article{1712251,
author = {Abhishek Kumar Singh, Aditya Chauhan, Aman Singh, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri},
title = {Movie Recommendation System Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {1914-1915},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712251.pdf},
abstract = {With the continuous expansion of digital streaming platforms, personalized movie recommendations have become essential to enhance user experience. This paper presents a machine-learning?based movie recommendation system utilizing content-based filtering, collaborative filtering, and hybrid approaches. The system is evaluated using the MovieLens dataset, and performance is measured using RMSE and MAE. The results indicate that hybrid models outperform individual approaches due to their ability to integrate user?item interactions with content features.},
keywords = {Recommendation System, Machine Learning, Collaborative Filtering, Content-Based Filtering, Hybrid Models, Movielens Dataset.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712251}
}