A Survey of Seasonal-based Movie Recommendations Using Machine Learning Through a Hybrid Approach with User Interest in Various OTT Platforms

Year : 2024 | Volume :11 | Issue : 01 | Page : 24-29
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 attention 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, while others focus on lead actors and directors. Having said that, a certain segment of the public continues to enjoy seeing such films. In this paper, the recommendation towards seasonal-based shows, such as Christmas eve, New Year, Valentine’s Day, Independence Day, Halloween, etc., are discussed using hybrid approach with user interest in various OTT (over-the-top) platforms.

Keywords: Recommendation system, seasonal, hybrid approach, over-the-top (OTT) platform, Machine learning, CBF algorithms

[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 Various OTT Platforms. Journal of Multimedia Technology & Recent Advancements. 2024; 11(01):24-29.
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 Various OTT Platforms. Journal of Multimedia Technology & Recent Advancements. 2024; 11(01):24-29. 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
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