Movie-Recommender-System

Built a content based recommender system using Cosine similarity.

Work Flow

1. Data Collection
2. Data preprocessing
3. Feature Engineering
4. User Input
5. Cosine Similarity algorithm
6. Output the list of recommended movies

Types of Recommender systems:

1. Content based Recommendation System: In this type of system, the algorithm recommends the movies depending on the content of the movie the user is watching. For example, if a user is watching a lot of superhero movies, the recommendations will be another set of superhero movies and so on.
2. Popularity Based Recommendation System: In this type of system, the algorithm will recommend movie based on the movies popularity. Movie popularity increases if more and more people watch the movies. It may also depend on the popularity of actors acted in the movie, directors and so on.
3. Collaborative Recommendation System: This type of system groups people based on their movie watching pattern. So, the algorithm will recommend movies based on the watching pattern of the user.