Accuracy Comparison of Various Movie Recommendation System Algorithms

A Movie Recommendation is significant in our social life since it enhances entertainment while also providing a tailored user experience. Using such recommendation algorithms, users may receive recommendations for a group of movies based on their interests or the popularity of the movies.Despite the fact that there are hundreds of movie recommendation systems, the majority of them either cannot recommend a movie to existing users or cannot propose a movie to a new user at all. In this paper, we compared the different algorithms to step up a movie recommendation system to improve the accuracy of user-recommended movies. This recommendation engine scours movie databases for all relevant variables, such as popularity and attractiveness, needed to make a recommendation. In many circumstances, large correlations between several categories of items can be used to produce more accurate suggestions. Experiments on real data have shown that the proposed solution is efficient and effective. This Movie Recommendation system will use a comparison among different algorithms that can be used to form a movie recommendation system, to search the movies that are similar to the customer’s taste or genre, and would also provide a combination of the recommendations that are highly rated by different customers’ experience so that the customer is not limited to a particular category or genre but is also able to explore new and different varieties as well. The system will get trained for the improvement of the recommendations through hybrid means.