Music Recommender Systems are computational techniques for suggesting music to an specific user according to his personal interest. They operate under a big amount of music files and, depending on the information provided in its entry, may apply Collaborative Filtering, Context-Based or Content-based approaches.
Collaborative Filtering makes recommendations to the current user based on items that other users with similar tastes liked in the past. Contextual Music Recommendation refer to the situation of the user when listening to recommended tracks (e.g, time, mood, current activity, the presence of other people). Music Content can be understood as musical features computed directly from audio, or semantic inferred or predicted by machine learning techniques.
Differently from another recommender systems, as for books, movies or news, the music ones has specific characteristics: they allow recommendation of repeated items, and has a fast consumption time in comparison. These leads us to differentiate between parallel (albums) and serial (playlist) recommendation.
Preliminary feature extraction results are finally presented, retrieved from a temporary database containing popular Brazilian music.