With over 13,000 titles on Netflix, there is an overwhelming number of entertainment options to choose from! As such, this learning project aims to create a simple content-based recommender system that can recommend TV shows and movies to a user. Of course, this is far more basic compared to industry standards but it is a fun personal project to work on!
We would take in an input which is a user's favourite show/movie and pick up the top 10 films that are most similar to his/her personal favourite. Here, we explore 2 possible ways to identify similar items: (1) A simple similarity measure - Cosine Similarity (2) Clustering Algorithm - Latent Dirichlet Allocation (LDA).
Data Set used: Kaggle Netflix Movies and TV Shows (https://www.kaggle.com/shivamb/netflix-shows)