ABSTRACT
The objective of this PhD research is to deepen the understanding of how people listen to music and construct playlists. We believe that further insights into such mechanisms can lead to enhanced music recommendations. We research on the exploitation of user-generated data in the context of on-line music services, since it constitutes a rich and increasing source of information of user behavior. The research carried out so far has centered on the scenario of producing a single artist recommendation. Concretely, in this paper we show how to mitigate the cold-start problem for new artists, elaborating on our findings on the combined effect of users' listening histories and users' tagging activity. As future research, we will investigate how improved techniques to exploit user-generated data can also be applied to the task of producing sequential recommendations, like playlists. We are particulary interested in creating music playlists similarly as users would do, and in finding mechanisms to make such music streams adapt to users' feedback on-line.
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Index Terms
Listener-Inspired Automated Music Playlist Generation
Recommendations
Biases in Automated Music Playlist Generation: A Comparison of Next-Track Recommending Techniques
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationPlaylist generation is a special form of music recommendation where the problem is to create a sequence of tracks to be played next, given a number of seed tracks. In academia, the evaluation of playlisting techniques is often done by assessing with the ...
Music Playlist Recommender System AFT-IS
ICCAE 2018: Proceedings of the 2018 10th International Conference on Computer and Automation EngineeringPreviously, we had proposed a playlist recommendation method to suggest music by considering the change in acoustic features in the song so that the transition between songs becomes smooth. Our previous method uses the last two songs in a playlist to ...
A novel music recommender by discovering preferable perceptual-patterns from music pieces
SAC '10: Proceedings of the 2010 ACM Symposium on Applied ComputingNowadays, advanced information and communication technologies ease the access of music pieces. However, it is still hard for the users to find what she/he prefers from a huge amount of music works. To solve this problem, most music recommenders based on ...
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