Using PCA and K-Means to Predict Likeable Songs from Playlist Information
Caroline Sharon Langensiepen, Adam Cripps, Richard John Cant · 2018
Most recommendation systems for music rely on individual song ratings. Current song recommendation software that uses playlists has shown to either be inaccurate or suggest songs that are extremely like those in the playlist already. Furthermore, this recommendation software tends to rely very large numbers of records. AI models are used to overcome these limitations using substantially less data. A collaborative filtering approach using two different models (K-means and hierarchical clustering) is used to separate playlist data into clusters for comparison. After the data has been clustered, a Euclidean distance measure is used between the songs in the cluster and the average values of the songs in a single users playlist to make the final predictions. The use of normalisation and PCA enabled the K-means and hierarchical clustering models to form clusters efficiently. When tested on a small sample of users, the system recommended songs that were considered likeable by the users 60% of the time, while still finding songs that were generally diverse.