All entertainment websites or online stores have millions/billions of items. It becomes challenging for the customer to select the right one. At this place, recommender systems come into the picture and help the user to find the right item by minimizing the options.Recommender System is a system that seeks to predict or filter preferences according to the user’s choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.
The dataset can be found here: https://grouplens.org/datasets/movielens/
The dataset used in this project contains 100,000 ratings and 3,600 tag applications applied to 9,000 movies by 600 users. The dataset used in this notebook includes two files :-
- movies
- ratings
The movies file has 3 features in it :-
- movieId
- Title
- Genres
The ratings file has 4 features in it :-
- userId
- movieId
- ratings
- timestamp
- Seaborn
- Scipy
- Numpy
- Pandas
- python 3
- scikit-learn
- most watched movies recommendations
- most rated movies recommendations
- Recommendations based on genres
- Recommendations of movies correlated to each other
- Recommendations of movies watched by common users