Paper Key : IRJ************686
Author: Amit Ghanata,Priyanshu Tiwari,Dhananjay Kapoor,Pushkar Saxena
Date Published: 10 Nov 2024
Abstract
Recommendation systems play a significant role in this digital world, especially in the entertainment world, where thousands of contents overwhelm the choices for users. A movie recommendation system is derived from the preferences, ratings, and behaviors of the users in choosing the most relevant movies. Paper Development of a Movie Recommendation System Based on Artificial Intelligence and Machine Learning Algorithms. Specifically, this report is based on two types of methods commonly used for creating personal recommendations: Collaborative Filtering (CF) and Content-Based Filtering (CBF). Both algorithms are experimented with on the Movielens dataset, indicating their ability to provide the audience with correct and diverse movie recommendations. As a byproduct of this research, we propose a hybrid approach that combines the two methods in order to overcome the deficiencies of each one separately. So, it implies that it performs better with hybrid model recommendations than with individual methods regarding the accuracy of the recommendations and the satisfaction of the user.