MOVIE RECOMMENDER SYSTEM USING MACHINE LEARNING

Abstract

The recommendation system of today has transformed the way we search for items of our interest through the use of an information filtering approach that predicts user preferences. A movie recommendation system named RECOM has been proposed in this paper. Based on the content-based filtering approach, it utilizes the information in the dataset, undergoes analysis, and recommends the most suitable movies for the user. The recommended movie list is ordered according to the IMDB ratings calculated by a standard formula. The system also enables users to search for movies based on their favourite actors/actresses. Developed in Python and Machine Learning, the recommender system generates recommendations through the utilization of various forms of knowledge and data about movies, such as vote count, vote average, mean, quantile, etc. Overall, the effectiveness and efficacy of movie searches have significantly increased thanks to the introduction of content-based filtering and machine learning techniques in movie recommendation systems, making it simpler for users to locate films they are likely to enjoy.