A Comprehensive Review on Video Recommendation System: Models, Challenges, and Applications

In today's digital era, Recommendation Systems play an indispensable role for individuals of all ages, guiding them towards videos, songs, games, cartoons, short videos, news, blogs, and more, tailored to their preferences.These systems assist users in navigating a vast array of available data, aiding them in discovering beneficial content.Recommendations cater to users' varying moods, requirements, and interests, fulfilling their desires for entertainment and information.With the burgeoning number of internet users, recommendation systems have become instrumental in saving valuable time that would otherwise be spent searching for the right content to consume.This paper provides an in-depth review of video recommendation systems, focusing particularly on the Netflix platform, which is effective for user engagement.Our research was performed using methodologies inspired by social network analysis.This paper addresses the challenges that Netflix faces in enhancing its recommendation system, exploring algorithms used in Content-based Filtering, Collaborative Filtering, and hybrid approaches.Netflix offers an extensive collection of television programs, including movies, web series, and animated content, that can be accessed online at any time.In this paper, we provided various techniques and used different approaches, especially for the Netflix movie recommendation system.We stimulated additional research interest in Netflix by exploring fundamental applications and challenges in developing the recommendation system.