Online streaming platforms like Netflix have plenty of movies in their repositories. If we can build a recommendation system to recommend movies to the users based on their historical interactions with movies, this would improve customer satisfaction. Increased customer satisfaction will increase the revenue of the company. The techniques that we will learn here will not only be limited to movies but can be any item for which you can build a recommendation system.
Using the above dataset, we will build two different types of recommendation systems that are listed below.
- Clustering-based recommendation system.
- Content-based collaborative filtering.
We will use the following three datasets for this case study:
-
ratings dataset - This dataset contains the following attributes:
- userId
- movieId
- rating
- timestamp
-
movies dataset - This dataset contains the following attributes:
- movieId
- title
- genres
-
tags dataset- This dataset contains the following attributes:
- userId
- movieId
- tag
- timestamp