Comparative Analysis of Movie Recommendation Systems

Recommendation systems are an important part of modern live-streaming services as they deliver personalized and relevant content to users. Machine learning algorithms have become integral to these systems because they can automatically learn from user behavior and provide accurate recommendations. Evaluating the machine learning algorithms used for recommendation systems in live streaming services is important to ensure that recommendations are accurate and effective. The recommendation system is derived from collaborative filtering, content-based, and hybrid-based approaches. These approaches will be used in the paper for finding the sentiments in movie recommendations and the further methods used are genetic algorithm and k means grouping.