My first recommendation systems. Made using sklearn, pandas and numpy. The data is taken from the Kaggle website.
The system for recommending movies is based on correlation. I use Pearson's correlation. Correlation Based Recommenders are a simpler form of collaborative filtering based recommenders. They give you more flavor of being personalized as they would recommend the item that is most similar to the item selected before.
This type of recommendation system uses an anime description to recommend the next most similar one. Content-based recommendations also provide "personalized" recommendations. The main difference between correlation-based recommendations and content-based recommendations is that the first system takes into account "user behavior" and then considers the content for recommendation. Content-based recommendations uses keywords in the description to find similarities between anime.