This project is a Music Recommendation System built using the Million Song Dataset and the Collaborative Filtering approach in Python. The system aims to provide personalized music recommendations to users based on their listening history and preferences.
The Million Song Dataset is a large collection of music data that includes information about songs, artists, and user interactions. It contains anonymized user listening data, such as user IDs, song IDs, and play counts, which are crucial for building the recommendation system.
http://millionsongdataset.com/
Collaborative Filtering is a popular recommendation technique that relies on user behavior data to make personalized recommendations. It identifies users with similar listening patterns and recommends songs that other users with similar tastes have enjoyed. There are two main types of Collaborative Filtering methods used in this project:
-
User-based Collaborative Filtering: It identifies similar users based on their listening history and recommends songs that the similar users have liked.
-
Item-based Collaborative Filtering: It identifies similar songs based on user interactions and recommends songs similar to those the user has already listened to.
Contributions to the Music Recommendation System are welcome. If you have any suggestions, improvements, or feature additions, please feel free to submit a pull request.