Abstract :

The Development Of Movie Recommendation Systems Began In The Late 1990s With The Rise Of E-commerce Platforms And Streaming Services. Early Methods Relied Heavily On Collaborative Filtering, Where Recommendations Were Based On User Behavior And Preferences. In The Past, Movie Recommendations Were Often Made By Friends, Family, Or Through Movie Critics And Television Programs. People Relied On Word-ofmouth, Printed Reviews, And Televised Recommendations To Decide What Movies To Watch. The Traditional System Of Movie Recommendations Was Limited By A Lack Of Personalization And Scalability. It Relied Heavily On Subjective Opinions And Could Not Cater To The Unique Tastes And Preferences Of Individual Users, Often Leading To Unsatisfactory Movie Choices. The Motivation Behind Developing Machine Learning-based Movie Recommendation Systems Is To Provide Personalized, Accurate, And Scalable Recommendations That Enhance User Satisfaction And Engagement. By Leveraging Vast Amounts Of Data, These Systems Can Uncover Patterns And Preferences That Are Not Immediately Apparent, Offering A More Tailored Viewing Experience. The Proposed System For Movie Recommendations Leverages Advanced Machine Learning Techniques To Provide Personalized Suggestions. It Integrates Collaborative Filtering, Content-based Filtering, And Hybrid Models To Analyze User Data And Predict Preferences. Collaborative Filtering Identifies Patterns In User Behavior By Comparing The Preferences Of Similar Users, While Content-based Filtering Analyzes Movie Attributes Such As Genre, Actors, And Directors To Match User Interests. A Hybrid Model Combines These Approaches To Enhance Recommendation Accuracy. User Interactions, Such As Viewing History, Ratings, And Search Queries, Are Continuously Collected And Processed. Machine Learning Algorithms, Including Matrix Factorization And Deep Learning, Are Employed To Detect Latent Factors And Complex Patterns Within The Data. These Models Are Trained And Fine-tuned To

Published:

09-6-2025

Issue:

Vol. 25 No. 6 (2025)

Page Nos:

82-95

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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