An adaptive algorithm to recommend favorable digital music

Many people enjoy digital music (e.g., MP3 songs), usually with random play mode, or their own favorable play list that they have composed. However, such play modes do not consider and support listener preferences of feeling or mood changing with time. Usually listeners have dynamic, not static, demands on music based on their arbitrary situation or mood (e.g., when studying, exercising, being sorrowful, being happy, etc.), so an adaptive algorithm to meet the momentary demand is required. This paper proposes an adaptive algorithm to recommend favorable songs successively, and enable people to seamlessly keep listening to favorable songs, without the action of skipping disliked ones. The algorithm monitors if a listener likes or dislikes a song currently being played. Once the algorithm detects that a listener likes the song, the algorithm recommends the next song that is most similar to the current song. Otherwise, the algorithm recommends quite a different style of a song as the next one, by recognizing that the listener now has a different demand. In our experiment, the proposed algorithm showed better performance, in terms of reducing the action of frequently skipping songs, than random play mode, with statistical significance.