There are basically three types of recommender systems:-
Demographic Filtering - They offer generalized recommendations to every user, based on movie popularity and/or genre. The System recommends the same movies to users with similar demographic features. Since each user is different , this approach is considered to be too simple. The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience.
Content Based Filtering - They suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. The general idea behind these recommender systems is that if a person liked a particular item, he or she will also like an item that is similar to it.
Collaborative Filtering - This system matches persons with similar interests and provides recommendations based on this matching. Collaborative filters do not require item metadata like its content-based counterparts.
In this repository I have made three python kernels explaining and implementing the different types of movie recommender systems.
Datasets - The Movie Dataset , TMDB Movie Dataset
To access the recommender systems you can fork this repository and run the python notebooks locally.