Music emotion recognition: the role of individuality
Proceedings of the international workshop on Human-centered multimedia
From MPEG-7 user interaction tools to hanging basket models: bridging the gap
Multimedia Tools and Applications
Pulling strings from a tangle: visualizing a personal music listening history
Proceedings of the 14th international conference on Intelligent user interfaces
An intelligent music playlist generator based on the time parameter with artificial neural networks
Expert Systems with Applications: An International Journal
Intelligent Music Playlist Recommendation Based on User Daily Behavior and Music Content
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Rush: repeated recommendations on mobile devices
Proceedings of the 15th international conference on Intelligent user interfaces
MusicalHeart: a hearty way of listening to music
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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Algorithms for automatic playlist generation solve the problem of tedious and time consuming manual selection of musical playlists. These algorithms generate playlists according to the user's music preferences of the moment. The user describes his preferences either by manually inputting a couple of example songs, or by defining constraints for the choice of music. The approaches to automatic playlist generation up to now were based on examining the metadata attached to the music pieces. Some of them took also the listening history into account. But anyway, a heavy accent has been put on the metadata, while the listening history, if it was used at all, had a minor role. Missings and errors in metadata frequently appear, especially when the music is acquired from the Internet. When the metadata is missing or wrong, the approaches proposed so far cannot work. Besides, entering constraints for the playlist generation can be a difficult activity. In our approach we ignored the metadata and focused on examining the listening habits. We developed two simple algorithms that track the listening habits and form a listener model--a profile of listening habits. The listener model is then used for automatic playlist generation. We developed a simple media player which tracks the listening habits and generates playlists according to the listener model. We tried the solution with a group of users. The experiment was not a successful one, but it threw some new light on the relationship between the listening habits and playlist generation.