Recommendation Engine Movies:

This type of recommendation systems, takes in a movie that a user currently likes as input. Then it analyzes the contents (storyline, genre, cast, director etc.) of the movie to find out other movies which have similar content. Then it ranks similar movies according to their similarity scores and recommends the most relevant movies to the user. We can say that two vectors are similar if the distance between them is small. By distance, we mean the angular distance between two vectors, which is represented by θ (theta). So we need to convert the required features into vectors. The CountVectorizer() class from sklearn.feature_extraction.text library can do this for us. We need to import this library before we can create a new CountVectorizer() object. Further, we applied Cosine Similarity to obtain Cosine Similarity Matrix. We can use this matrix to obtain similar movies.