This model recommends song for you based on the similarity of two song contents or attributes.
A recommender (or recommendation) system (or engine) is a filtering system which aim is to predict a rating or preference a user would give to an item, eg. a film, a product, a song, etc.
Content-based methods are computationally fast and interpretable. Moreover, they can be efficiently adapted to new items or users. However, one of the biggest limitations of content-based recommendation systems is that the model only learns to recommend items of the same type that the user is already using or, in our case, listening to. Even though this could be helpful, the value of that recommendation is significantly less because it lacks the surprise component of discovering something completely new.
The aim of this project is to:
Generate a content-based music recommender system using a dataset of name, artist, and lyrics for 57650 songs in English obtained from Kaggle. The data has been acquired from LyricsFreak through scraping by the author.