Music playlists are appreciated by users, music artists and service providers for various reasons (i.e., no need to waste time choosing what to listen to, showcase to increase popularity, engage users to the provided services). However, despite their ever-increasing centrality, in literature there is no precise definition on how to produce them. Often, playlists are produced by music recommendation algorithms that focus on the songs selection process and don't give enough importance to songs sequencing. Indeed, until a few years ago the listening order was not considered important. In this paper, we address the songs sequencing problem in a novel way. Through dynamic programming, we transform a set of non-ordered songs into a user-tailored sequence of songs that meets the user's musical preferences. To the best of our knowledge, this approach has never been used in the literature.
Automatic and Personalized Sequencing of Music Playlists / Furini, M.; Montangero, M.. - (2022), pp. 205-208. (Intervento presentato al convegno IEEE 42nd International Conference on Distributed Computing Systems Workshops - ICDCSW 2022 tenutosi a Bologna nel luglio 2022) [10.1109/ICDCSW56584.2022.00046].