Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist Continuation

The broad diffusion over the Internet of songs streaming services points out the need for implementing efficient and personalized strategies for incrementing the fidelity of the customers. This scenario can collect enough information about the user and the items for successfully design a Recommender System for the automatic continuation of playlists of digital contents. In particular, in this work we proposed a strategy for suggesting a set of tracks, starting from a list of songs played by the user, candidate as next to play. The list contains songs that are coherent with the main characteristics of songs already played. In order to collect enough information and for applying a recommendation strategy, we used third-party external sources of information. They provide data about the song, including its popularity, the emotion evoked by its lyrics, low and high-level audio features, lyrics and more. The system highlights the importance to use user-generated tags and emotional features for successfully predicts user next played songs.