Movie Recommender System

Recommender systems are necessary in current time, since the information available online can be overloading to a user. These systems are used everywhere, starting from the online shops to the websites that are focused on recommending particular item, such as videos to watch or songs to listen to. Recommender system that predicts the likings of a user based on their previous behavior is very popular when it comes to picking up the movies to watch. This paper talks more about the movie recommender systems, and explains the way that different types of recommendations can be used in order to test datasets and provide good recommendations for variety of users. are more likely to find interesting by guiding them towards that product more. Not just that they helped in the shopping field, but also in guiding user towards the videos that could be of his or hers interest, as well as songs or movies [1]. Users are more likely to watch the movie that has been suggested by the recommender system, or see a vide YouTube that was placed below the “Recommended” label. Since of the overwhelming information and constant movie releases, people also try to use recommender systems in order to find the movie that could be similar to those of their likings, or the movies that presents cast and plots that would be of their interest. This paper talks more about movie recommender systems and their types. It is also focused on testing out simple recommender system for movies that gives the user different types of recomm the data available. o on