Content based Movie_recommendation_system

• At some point each one of us must have wondered where all the recommendations that Netflix, Amazon, Google give us, come from. We often rate products on the internet and all the preferences we express and data we share (explicitly or not), are used by recommender systems to generate, in fact, recommendations.

• It turns out that there are (mostly) three ways to build a recommendation engine:

  1. Popularity based recommendation engine
  2. Content based recommendation engine
  3. Collaborative filtering based recommendation engine

• Among the 3 types of recommendation engines, I have built a content-based recommendation engine using Python and Scikitlearn. - I first understood basic concepts such as cosine distance, euclidean distance and when to use each of them.

• Finally, by using IMDB 5000 movie dataset built a content-based recommendation engine using CountVectorize and Cosine similarity scores between movies.

• References: https://www.youtube.com/watch?v=XoTwndOgXBM&t=4820s https://medium.com/@sumanadhikari/building-a-movie-recommendation-engine-using-scikit-learn-8dbb11c5aa4b