ABSTRACT

As users accumulate digital music in their digital devices, the problem arises for them to manage the large number of tracks in their devices. If a device contains thousands of tracks, it is difficult, painful, and even impractical for a user to pick suitable tracks to listen to without using predetermine organization such as playlists. The topic of this thesis is computationally generated recommendations. Music recommendation is significantly different from other types of recommendations, such as those for movies, books and electronics. Because a same song can be recommended to a same users many times if we successfully keep from him/her bored with it. A main purpose of a music recommendation system is to minimize user's effort to provide feedback and simultaneously to maximize the user's satisfaction by playing appropriate song at the right time. Reducing the amount of feedback is an important point in designing recommendation systems, since users are in general lazy. We can evaluate user's attitude towards a song by examine whether the user listens to the song entirely, and if not, how large a fraction he/she does. In particular, we assume that if the user skips a recommended song, it is a bad recommendation, regardless of the reason behind it. If the recommended song is played completed, we infer that the user likes the song and it is a satisfying recommendation. On the other hand, if the song is skipped while just lasting a few seconds, we conclude that the user dislikes the song at that time and the recommendation is less effective.