A Survey of Seasonal-Based Movie Recommendations Using Machine Learning through a Hybrid Approach with User Interest in the various OTT Platform

Year : 2024 | Volume :11 | Issue : 01 | Page : –
By

    B. Sriram

  1. PG Student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar College of Engineering and Technology, Puducherry, India

Abstract

No matter their age, gender, race, color, or region, everyone enjoys watching films particularly during festival season. We are all, in the most basic sense, connected to one another through this beautiful medium, but what really grabs notice is the fact that, regardless of how unique our choices and combinations are in terms of picture show preference, one thing remains constant. Certain people have a preference for certain types of movies, such as romance, sci-fi, or thrillers. Others focus on lead actors and administrators, in contrast. Having said that, a certain segment of the public continues to enjoy seeing such films. In this paper, the recommendation towards seasonal based such as Christmas eve, New year, Valentine’s Day, Independence Day, Halloween, etc., using hybrid approach with user interest in the various OTT (over-the-top) platform.

Keywords: Recommendation System, Seasonal, Hybrid Approach, OTT Platform

[This article belongs to Journal of Multimedia Technology & Recent Advancements(jomtra)]

How to cite this article: B. Sriram.A Survey of Seasonal-Based Movie Recommendations Using Machine Learning through a Hybrid Approach with User Interest in the various OTT Platform.Journal of Multimedia Technology & Recent Advancements.2024; 11(01):-.
How to cite this URL: B. Sriram , A Survey of Seasonal-Based Movie Recommendations Using Machine Learning through a Hybrid Approach with User Interest in the various OTT Platform jomtra 2024 {cited 2024 Apr 04};11:-. Available from: https://journals.stmjournals.com/jomtra/article=2024/view=138591


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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 16, 2024
Accepted February 27, 2024
Published April 4, 2024